proceedings esav'11

Proceedings of the

TYRRHENIAN INTERNATIONAL WORKSHOP ON DIGITAL COMMUNICATIONS

ENHANCED SURVEILLANCE OF AIRCRAFT AND VEHICLES

September 12 – 14, 2011 - CAPRI, Italy

Organised by

CNITConsorzio Nazionale Interuniversitario per le Telecomunicazioni

AVV Associazione Vito Volterra

UNIVERSITÀ DEGLI STUDI DI ROMATOR VERGATA

ii

Edited by

Gaspare Galati Tor Vergata University, Roma

Piet van Genderen Delft University of Technology / IRCTR, Delft, The Netherlands

Enhanced Surveillance of Aircraft and Vehicles – 2011 Workshop Proceedings

Published by Centro Vito Volterra – Tor Vergata UniversityISBN: 978-88-903482-3-5

Copyright © Associazione Vito Volterra 2011

Materials contained in the Enhanced Surveillance of Aircraft and Vehicles – 2011 Workshop Proceedings are copyrighted and are protected by worldwide copyright laws and treaty provisions. Except as otherwise stated herein, none of the material contained in the Enhanced Surveillance of Aircraft and Vehicles – 2011 Workshop Proceedings may be copied, reproduced, distributed, republished, downloaded, displayed, posted or transmitted in any form or by any means without the prior written permission of the copyright holder. Permission is granted to display, copy and distribute the materials for personal, non-commercial use only, provided that materials are not modified and that all copyrighted and other proprietary notices contained in the materials are retained. This permission terminates automatically upon breach any of these terms or conditions. Upon termination, printed, copied and downloaded materials must immediately be destroyed.

iii

Preface

The need for increasing safety and efficiency levels in the air transport system requires modern control andtraffic management (ATM) systems for aircraft in air and in ground operations, as well as for servicevehicles on the airport surface. The related Communications, Navigation and Surveillance (CNS)infrastructures call for enhanced positioning and identification techniques such as Multilateration (MLAT)and Wide Area MLAT (WAM), automatic dependent surveillance broadcast (ADS B), automatic location ofvehicles and management (AVMS). This type of enhanced surveillance infrastructure is spatially distributed(i.e. with many receiving or transmitting/receiving stations) and logically distributed (i.e. with local andcentral processing and with fusion of different information sources, including the traditional primary andsecondary radar).In this frame, new system architectures and new algorithms for integrity monitoring and for multi sensordata fusion are required.Security and defence systems use similar algorithms for passive locating of targets based on measurementsof Time of Arrival (TOA) and its differences (TDOA) as well as of Doppler frequency and its differences(FDOA), possibly combined with angular/direction measurements (AOA/DOA).The emerging “dual use” concept for surveillance and identification systems and the intrinsiccommonalities of location algorithms in the different applications did suggest to include the pertainingitems in the Workshop’s topics.

These enhanced surveillance systems are considered in their many facets:

ADS B implementation, services, equipage and applicationsADS B at airportsWide Area MultilaterationMultilateration at AirportsRadar technology for airport surveillancePSR and SSR technology for Air Traffic SurveillanceGNSS applications to Air Traffic ManagementSafety issues, solutions and standardsInteroperability between commercial, military and General AviationIntegration of unmanned aircraft systems (UAS)Dual use applications (security, defence)Systems and Subsystems: Architectures, New conceptsPassive location based on time, Doppler, angle MeasurementsSensor data fusionTechnologies (hardware, firmware, software)Environmental aspects (including radio propagation)Testing and Field Analysis, Integrity MonitoringImplementation plans and Operational results

This workshop is aimed to cover all of these facets, at various levels of detail. We trust that it will provide atrue flavour of the current developments and trends, and therefore serve the best interests of theoperational and scientific communities.

Gaspare Galati Piet van Genderen

iv

Members of the Technical Program Committee

Simon Atkinson, Roke Manor Research Ltd U.K.Juan Vicente Balbastre Tejedor, Technical University ValenciaCristiano Baldoni, ENAV ItalyJuan Besada, Technical University MadridJochen Bredemeyer, Flight Calibration Services GermanyRonald Bruno, ITT USASteven Campbell, MIT Lincoln LabEnzo Dalle Mese, University of Pisa ItalyPatrizio De Marco, Selex S.I. ItalySergio di Girolamo, Thales Alenia Space ItalyPietro Finocchio, AFCEA ItalyMichele Fiorini, IET ItalyPeter Form, Technical Univewrsity BraunschweighGaspare Galati, Tor Vergata University Rome ItalyFulvio Gini, University of Pisa ItalyRalf Heidger, DFS GermanyAdam Kawalec, Military University of Technology Warsaw PolandDirk Kugler, DLR GermanyMauro Leonardi, Tor Vergata University Rome ItalyIan Levitt, FAA USAKonstantin Lukin, IRE Kharkov UkrainePravas Mahapatra, Indian Institute of Science BangaloreMichel Moruzzis, Thales FranceDaniel Muller, Thales Air Systems FranceBenito Palumbo, Private Expert Consultant ItalyGabriele Pavan, Tor Vergata University Rome ItalyGiorgio Perrotta, Space Systems ItalyNicolas Petrochilos, University of Reims FranceDaniela Pistoia, Elettronica S.p.A. ItalyChristos Rekkas, EurocontrolHermann Rohling, Techn. University Hamburg HarburgJohn Scardina, FAA USAFausto Simoni, ENAV ItalyRoberto Sorrentino, Perugia University and EUMADaniel Stamm, SkyguideLorenz Peter Schmidt, Universitaet Erlangen Nuernberg GermanyVojtech Stejskal, ERA Beyond Radar – Pardubice Czeck RepublicFilippo Tomasello, EASA ColognePiet van Genderen, Technical University DelftCarlo Vertua, Thales ItaliaFelix Yanovski, University of KievAlex Yarovoy, Technical University Delft IRCTR, The NetherlandsMaurizio Zacchei, ENAV Italy

v



LIST OF SESSIONS

1 - SESAR 1 Chair: C. BaldoniCo-chair: R. Heidger

2 - REGULATORY AND OPERATIONAL ASPECTS 33 Chair: P. FinocchioCo-chair: C. Rekkas

3 - HIGH RESOLUTION RADAR TECHNIQUES 59 Chair: J. BesadaCo-chair: D. Kügler

4 - NON-COOPERATIVE LOCATION 87 Chair: D. PistoiaCo-chair: A. Kawalec

5 - NON-COOPERATIVE PASSIVE COHERENT LOCATION AND

MULTISTATIC PRIMARY SURVEILLANCE RADARLOCATION 117 Chair: D. MullerCo-chair: V. Stejskal

6 - NEW CONCEPTS AND TOOLS 139 Chair: G. PavanCo-chair: A. Mahapatra

7 - MULTILATERATION AND ADS-B (1) 165 Chair: S. Di GirolamoCo-chair: S. Atkinson

8 - MULTILATERATION AND ADS-B (2) 195 Chair: S. AtkinsonCo-chair: S. Di Girolamo

INTERACTIVE SESSION 223 Chair: P. van GenderenCo-chair: M. Leonardi

vii

Focused Session 1 SESAR

Chair: C. Baldoni • Co chair: R. Heidger

The frame of this session is the provision of surveillance, navigation and separation in a highlyintegrated ground based and airborne future ATM System aimed at greener, more efficient, more

cost effective and all safe air transport operations.

From identification of requirements to the operational validationof an integrated solution 2

Claudio Vaccaro, Gabriella Duca - SICTA, Italy

A new concept for ATM Service Supervision for the new SESAREnterprise Architecture 9Gabriella Carrozza - SESM s.c.a.r.l., ItalyStephen Straub - Deutsche Flugsicherung GmbH (DFS), GermanyHakim Souami - THALES Air Systems, France

ADS B Integration in the SESAR surface surveillance architecture 13 Andrés Soto, Pedro Merino, Jorge Valle - Indra Sistemas S.A., Spain

Future mobile satellite communication 19 Pierpaolo Tavernise - THALES Alenia Space, Italy

Optimising Runway Throughput through Wake Vortex Detection,Prediction and Decision Support Tools 27Frédéric Barbaresco, Philippe Juge, Mathieu Klein, Yves Ricci,

Jean-Yves Schneider, Jean-François Moneuse - THALES Air Systems, France

Session 2 Regulatory and Operational Aspects

Chair: P. Finocchio • Co chair: C. Rekkas

ADS B and WAM deployment in Europe 35 Christos Rekkas - Eurocontrol, Belgium

U.S. Activities in ADS B Systems Implementation 41 Paul Douglas Arbuckle - US Federal Aviation Administration, USA

Detect and avoid for Unmanned Aircraft Systems inthe total system approach 47Filippo Tomasello - EASA, ItalyDavid Haddon - EASA, Germany

viii

North Sea Helicopter ADS B/MLat Pilot Project Findings 53Paul Thomas - Bristow Helicopters, United Kingdom

Session 3 High Resolution Radar Techniques

Chair: J. Besada • Co chair: D. Kügler

LAOTSE, an Approach for Foreign Object Detectionby multimodal netted 2D / 3D Sensors 61Sebastian Hantscher, Helmut Essen, Paul Warok Ruediger Zimmermann, Martin Schröder, Rainer Sommer, Stefan Lang - Fraunhofer – FHR, GermanyMarek Schikora, Klaus Wild, Wolfgang Koch - Fraunhofer – FKIE, Germany

Millimeterwave Radar for Runway Debris Detection 65Helmut Essen, Frank P. Lorenz, Sebastian Hantscher, Paul Warok, Ruediger Zimmermann, Martin Schröder - Fraunhofer – FHR, GermanyWolfgang Koch, Marek Schikora, G. Luedtke, Klaus Wild - Fraunhofer – FKIE, Germany

OFDMWaveforms for a Fully Polarimetric Weather Radar 69 Oleg A. Krasnov, Zongbo Wang, R. Firat Tigrek, Piet van Genderen - Delft University of Technology, The Netherlands

Polarimetry applied to avionic weather radar: improvement onmeteorological phenomena detection and classification 73

Alberto Lupidi, Christian Moscardini, Fabrizio Berizzi - University of Pisa, Italy Andrea Garzelli - University of Siena, Italy Fabrizio Cuccoli - CNIT, Italy Marcello Bernabò - SELEX Galileo S.p.A., Italy

Principles of Utilization of Polarization Invariant Parameters forClassification and Recognition of Distributed Radar Objects

Part I. Simplest model of a distributed object paper 79 Victor N. Tatarinov, Sergey V. Tatarinov

Tomsk University of Control Systems and Radioelectronics, Russian Federation

Piet van Genderen - Delft University of Technology, The Netherlands

Part II. Multipoint model and correlation theory 83 Victor N. Tatarinov, Sergey V. Tatarinov

Tomsk University of Control Systems and Radioelectronics, Russian Federation

Piet van Genderen - Delft University of Technology, The Netherlands

ix

Focused Session 4 Non-cooperative location

Chair: D. Pistoia • Co chair: A. Kawalec

Scope of the session is to collect state of the art results related to the problem of location of noncooperative targets. Particular emphasis will be given to all those papers addressing passive

techniques, both stand alone and integrating information from active sensors.Processing of signals related to the measurement of radiofrequency and/or IR/VIS/UV signature are

of interest. Papers showing results from trials will have priority.

Enhancing sensitivity for emitter geolocation 89 Göran Tengstrand, Viktor Andersson, Peter Hultman - SAAB Electronic Defence Systems, Sweden Dario Benvenuti - ELETTRONICA, Italy Jean-François Grandin, Luc Bosser - THALES Systèmes Aéroportés, France Börje Andersson, Anders Johansson - Swedish Defence Research Agency, Sweden

New solution to enhance the security in Air Traffic Control 95 Enrico Anniballi, Roberta Cardinali - SESM s.c.a.r.l., Italy

Solving the Data Link bottleneck for MPEG Location 101 Jean-François Grandin, Luc Bosser - THALES Systèmes Aéroportés, France Göran Tengstrand, Viktor Andersson, Peter Hultman - SAAB Electronic Defence Systems, Sweden Dario Benvenuti - ELETTRONICA, Italy Börje Andersson, Anders Johansson - Swedish Defence Research Agency, Sweden

Parasitic Doppler effect in passive location 107 Dario Benvenuti - ELETTRONICA, Italy

An in air passive acoustic surveillance system for air traffic control 111 Domenico Donisi, Marco Bonamente - D'Appolonia S.p.A., Italy Vincenzo Quaranta, Salvatore Ameduri - CIRA, Italy

Focused Session 5 Non-cooperative Passive Coherent Location and Multistatic Primary Surveillance Radarlocation

Chair: D. Muller • Co chair: V. Stejskal

The session is aimed to discuss the progress of Passive Coherent Location and other independentnon cooperative surveillance techniques based on radar, like multi static primary radar.

On board PCL systems for airborne platform protection 119 Krzysztof S. Kulpa, Mateusz Malanowski, Piotr Samczy ski, Jacek Misiurewicz - Warsaw University of Technology, Poland Maciej Smolarczyk - Telecommunications Research Institute, Poland

x

FM Based Passive Coherent Radar.From detections to tracks 123Radek Plšek, Vojt ch Stejskal, Martin Pelant,

Martin Vojá ek - ERA Beyond Radar, Czech Republic

High Range Resolution Multichannel DVB T Passive Radar:Aerial Target Detections 129

Dario Petri, Amerigo Capria - CNIT, Italy Michele Conti, Fabrizio Berizzi, Marco Martorella, Enzo Dalle Mese - University of Pisa, Italy

Range Only Multistatic Tracking in Clutter 133Darko Mušicki, Taek Lyul Song - Hanyang University, Republic of Korea

Session 6 New Concepts and Tools

Chair: G. Pavan • Co chair: A. Mahapatra

Tracker Quality Monitoring by Non Dedicated Calibration Flights 141Matthias Hess, Ralf Heidger - Deutsche Flugsicherung GmbH (DFS), GermanyJochen Bredemeyer - FCS Flight Calibration Service GmbH, Germany

The Transponder Data Recorder: first implementation and applications 147Gaspare Galati, Mauro Leonardi, Emilio G. Piracci - Tor Vergata University, ItalySoumem Samanta - National Institute of Technology, India

ADS B/MLAT surveillance system from High Altitude Platform Systems 153Mauro Leonardi, Silvio Spinelli, Gaspare Galati - Tor Vergata University, Italy

Space based ADS B A small step for technology a giant leap for ATM? 159Adam Parkinson - Helios, United Kingdom

Focused Session 7 Multilateration and ADS-B (1)

Chair: S. Di Girolamo • Co chair: S. Atkinson

Aim of the session is a technical discussion on the advantages and limitations of separate WAM andADS B systems and the benefits and technical challenges of data fusion in combined systems.

Strategies to Design and Deploy Mode S Multilateration Systems 167 Ivan A. Mantilla-G, Juan V. Balbastre-T,

Elías de los Reyes - Universidad Politécnica de Valencia, Spain Mauro Leonardi, Gaspare Galati - Tor Vergata University, Italy

xi

Correction of systematic errors in Wide Area Multilateration 173Jorge Abbud, Gonzalo de Miguel,Juan Besada - Universidad Politécnica de Madrid, Spain

Multilateration system time synchronization viaover determination of TDOA measurements 179Martin Pelant, Vojt ch Stejskal - ERA Beyond Radar, Czech Republic

Improvement of Multilateration (MLAT) Accuracy and Convergencefor Airport Surveillance 185Ivan A. Mantilla-G, Juan V. Balbastre-T, Elías de los Reyes - Universidad Politécnica de Valencia, SpainMauro Leonardi, Gaspare Galati - Tor Vergata University, Italy

Assessing the safety of WAM over a non radar surveillance area 191James Hanson, Ben Stanley - Helios, United Kingdom

Focused Session 8 Multilateration and ADS-B (2)

Chair: S. Atkinson • Co chair: S. Di Girolamo

Aim of the session is a technical discussion on the advantages and limitations of separate WAM andADS B systems and the benefits and technical challenges of data fusion in combined systems.

Implementation of ADS B SystemsBenefits and Considerations 197Abraham Barsheshat - Sensis Corporation, USA

Investigation of Measurement Characteristics ofMLAT / WAM and ADS B 203Klaus Pourvoyeur, Adolf Mathias, Ralf Heidger - Deutsche Flugsicherung GmbH (DFS), Germany

Real Time Performance Monitoring and NoiseAnalysis in an operational WAM System 207Alexander Pawlitzki, Holger Neufeldt - THALES Air Systems GmbH, Germany

ADS B via Iridium NEXT satellites 213Paolo Noschese, Silvia Porfili, Sergio Di Girolamo - THALES Alenia Space, Italy

xii

Independent Surveillance Broadcast – ADS B Receiverswith DOA Estimation 219Christoph Reck, Lorenz-P. Schmidt Max S. Reuther - University of Erlangen-Nuremberg, GermanyAlexander Jasch - TU Braunschweig, Germany

INTERACTIVESESSION

Chair: P. van Genderen • Co chair:M. Leonardi

SBAS Availability Improvement Based on theModified Radar Techniques 225Boriana VassilevaInstitute for Information and Communication Technologies, Bulgaria

Boris Vassilev - Technical University of Sofia, Bulgaria

Smart concatenation of Correlative Direction Finding andSuper resolution techniques 231Libero Dinoi, Marco Guerriero, Gianpiero Panci - Elettronica SpA, Italy

Development of High Performance WAM System 237Hiromi Miyazaki, Tadashi Koga, Eisuke Ueda, Yasuyuki Kakubari, Shirou Nihei - Electronic Navigation Research Institute, Japan

Machine Readable Encoding Standard Specifications in ATC 241Adolf Mathias, Matthias Hess - Deutsche Flugsicherung GmbH (DFS), Germany

Experimental ADS B based surveillance 247Juan A. Besada, Gonzalo de Miguel Ana M. Bernardos, José R. Casar - Universidad Politécnica de Madrid, Spain

ADS B HILS Test for Collision Avoidance of Smart UAV 253Changsun Yoo, Am Cho, Bumjin Park Youngshin Kang - Korea Aerospace Research Institute, Republic of Korea Sangwook Shim, Ilhyung Lee - Korea Advanced Institute of Science and Technology, Republic of Korea

xiii

A genetic algorithm and local search for the automatedcalculation of dilution of precision of mode Smultilateration systems at airports 259Ruy F.Ruiz-Mojica, Ivan A. Mantilla-Gaviria,Emilio J. Gómez-Pérez, Juan V. Balbastre-Tejedor, Elias de Los Reyes-Davó - Universidad Politécnica de Valencia, Spain

Multi Approach Strategy for Multi Sensor DataFusion Enhancement 265Carlo A. Vertua, Luca Saini - THALES, ItalyOlivier Baud, Nicolas Honoré, Peter E. Lawrence - THALES, France

Automatic Identification of Process Steps inMultilateration Data 271Stefanie Helm - German Aerospace Center (DLR), Germany

LIST OF AUTHORS 277

SESAR

From identification of requirements to the operationalvalidation of an integrated solution

Session 1.1 page 3

A new concept for ATM Service Supervisionfor the new SESAR Enterprise Architecture

Session 1.2 page 9

ADS B Integration in the SESAR surfacesurveillance architecture

Session 1.3 page 13

Future mobile satellite communicationSession 1.4 page 19

Optimising Runway Throughput through WakeVortex Detection, Prediction and Decision Support Tools

Session 1.5 page 27

Proceedings of ESAV'11 - September 12 - 14 Capri, Italy 1

From identification of requirements to the operational validation of an integrated solution

Approach and issues to design an effective Human Machine Interface for air traffic controller working position in SESAR

Claudio Vaccaro SICTA

Sistemi Innovativi per il Controllo del Traffico Aereo Operational Concept and Studies

Naples - ITALY [email protected]

Gabriella Duca SICTA

LEAS – University Federico II Naples - ITALY [email protected]

Abstract - Design, development and implementation of advanced technological systems is the challenging business experienced by industry engineers in every day professional life. The increased complexity of such systems and the huge amount of information to be managed makes the challenge even more hard. The new European Air Traffic Management System under development in the framework of the SESAR Programme represents one of these complex system of systems where a number of different stakeholders distributed worldwide are required to interact with. A primary actor taking safety-critical decision in the ATM is the Air Traffic Controller. Thus, the availability of timely and clear information allowing the Human to take the best decision any time in any condition is vital. That means, in turn, the Human Machine Interface (HMI) of the Air Traffic Controller Working Position (ATCo CWP) plays a very essential and strategic role. The paper intends to explain how SESAR project P05.09 “Usability Requirements and Human Factors Issues for the Controller Working Position” is going to address the requirements definition process and planning pre-operational validation activity for the SESAR CWP HMI.

Keywords-component; SESAR; ATM; CWP; HMI; Human-Machine Interface; Human Factors; Safety

I. INTRODUCTION

The mission of SESAR is to develop a modernized air traffic management system for Europe, which will prevent crippling congestion of the European sky, enhance the performance of Air Traffic Management (ATM) and reduce the environmental impact of air transport.

SESAR concept relies on new cross-cutting paradigms with expected changes in actors’ role, new way of sharing responsibility between concerned stakeholders and also dynamically allocating functions between human operators and system. As a basic principle, in SESAR concept Human is considered central to the operation in the future European ATM system environment and still being the ultimate decision-

maker. On the other hand, latest progress on emerging technology, new tools and automation are expected to facilitate human day-to-day duties. Last, not least, Safety is one of the key performance targets in SESAR, with the objective to improve safety performance by a factor of ten while air traffic is expected to triple. And very finally, there are a number of relevant issues to solve in addressing local specific user needs within a single wide European perspective programme.

This is the very high level scenario where SESAR engineers have been moving to design and evaluate an effective CWP HMI, taking into account a broad amount of constraints at various levels to achieve the goal. Relevant aspects of the intended approach and expected issues are outlined in this paper.

II. REQUIREMENTS DEFINITION PROCESS

Several Operational Concept elements, with associated Operational Improvements, have been considered for operational implementation within the SESAR timeframe. Related research and development issues are addressed by relevant projects under the overall coordination of the SESAR Joint Undertaking (SJU). To facilitate the harmonization within the programme, development of such concepts is performed following particular guidelines derived in a first instance from the EUROCAE ED78A/RTCA DO-264 “Guidelines for approval of the provision and use of Air Traffic Services supported by data communications” [1]. Additionally, the need of ensuring consistency in the requirements production is accomplished by means of structured documentation based on ad-hoc layout templates/tools and exchanged through commonly accessible shared requirements repository. All this implies any SESAR stakeholders is able to write unambiguous requirements that will be consistently understood and referenced with unique identifier in the whole programme, thus ensuring cross-reference and traceability issues.


OperatorsRequirements

Business Requirements

Operational need Requirements

Operational Requirements

PrototypesRequirements

System Requirements

Solution Requirements

BRR

OCDCONOPSAIRM

DOD

ISRM

ADDTS

TSICD

OSEDSPRINTEROP

Need

Solution

Prototyping

ADD

Procedure

An operational scenario will be then represented by integrating & merging requirements coming from different

projects investigating those operational concepts interacting within that Operational Package/Sub-Package. As a consequence, harmonization and consolidation of requirements are necessary to ensure both technical cohesion and unambiguous understanding of role and responsibility of each concerned actor.

Figure 1. Operational Requirements in a SESAR Project

For that reason, the SESAR Work Programme is structured around a set of Operational Packages and Sub-Packages [3]. A third grouping – Operational Focus Area (OFA) - based on common operational focus and linked to the Operational Sub-Package, was defined to ensure dependencies are respected leading to coherent and integrated validation activities and associated results.

Figure 2. Operationa Packages, sub-packages and Focus Areas

However, some OFA are of transversal nature and the controller HMI is surely one this. That means, the project P05.09 is from one side a project of those contributing to the iCWP En-Route and TMA OFA, but on the other hand it is linked to most/all other packages to ensure the conceptual

consistency of operational requirements and validation strategy fro the CWP HMI.

The main objectives of 05.09 project are indeed:

• Effective definition of Controller Working Position User Requirements to allow a proper design of the En-Route and TMA Controller Working Position, including the related Human-Machine Interface

• Assess, through extensive validation campaigns, that the controller is safely capable of managing, operating and interacting with the complex SESAR ATM system

In the end of the process, the expected result is aimed to have one consistent, integrated, HMI across all E-Route and TMA services.

In detail, the project 5.9 will be gathering Requirements (Operational, Safety & Performance, Interoperability, Human Factors) from other operational projects/packages/sub-packages. Requirements which may have a need to be considered from the controller HMI perspective are then grouped and harmonized for prioritization to be iteratively refined also by means of a number of mock-ups. HMI solutions meeting those Requirements will then assessed in extensive validation campaigns through pre-industrial prototypes developed by “mirror” technical projects (namely P10.10.03).

Figure 3. Information displayed on controller HMI

Practices, principles and assumptions of the SESAR validation strategy are expanded and detailed further in order to derive a iCWP HMI detailed validation strategy and plan thus ensuring that the exercises are both locally and globally consistent. The validation of the integrated CWP HMI will be performed from an operational, human factors and safety perspective for each of the three operational steps identified in the SESAR Concept Story Board (Step 1 Time based operations (2013), Step 2 Trajectory based operations (2017) and Step 3 Performance based operations (2020)).

The intended project validation strategy is building on a iterative-incremental approach based on the following steps:

4 Proceedings of ESAV'11 - September 12 - 14 Capri, Italy

• Production of an initial set of iCWP HMI operational, SPR and human factors requirements supported by description of detailed use cases

• On the basis of those initial requirements, early validation activities (V2) will be planned and executed by extensive use of mock-ups

• The concept under assessment is refined and then further validated by means of Real Time Human in the loop validation exercises based on early release of iCWP prototypes provided by P10.10.03

• Following iterative refinements and assessments, the ultimate stage of the validation activity will be performed using the final release of iCWP industrial prototypes released by the P10.10.03

• Finally, taking in due account both validation results and latest available OSEDs, a consolidated set of iCWP HMI requirements will be delivered, thus closing the feedback loop.

III. REQUIREMENTS VALIDATION AND VERIFICATION PROCESS

A. The V&V perspective in the SESAR project P05.09 Validation is “an iterative process by which the fitness for

purpose of a new system or operational concept being developed is established.” This focuses on proving that the concept is “fit for purpose” and aiming to answer the question “are we building the right system?”

“Verification” provides proof that technology components are feasible and can be safely and economically implemented, so verification can be defined in the same manner to validation, focusing on technology and aiming to answer the question; “are we building the system right?”

As SESAR focus is on operational implementation, validation and verification processes will be focused on preparing for implementation, working close to actual operations and concentrating on reducing time to market of procedures, technology and early identification of “quick wins.”

Taking into account the ICAO Based Key Performance Areas and associated SESAR key Performance Indicators, iCWP HMI validation and verification activities shall concentrate on the

• Operational Usability & other Human Factors issues: demonstrate how all services can be “easily” provided and “friendly” managed, that the iCWP HMI is usable and accepted by air traffic controllers (end-users) with no negative safety/human workload effects;

• Technical Feasibility: demonstrate that a product or system is properly designed, is ready to be operationally and technically implemented and that transition issues are well understood and managed;

• Safety: the end-user of SESAR ATM system (controller) shall be capable of safely interact with the system without reducing current level of Safety.

The HMI validation approach shall be based on both objective and subjective measures of controllers performance. Details for Safety and Human Factors issues are provided below. It is anticipated that the validation against user needs will be performed in a pre-operational environment, fully representative of both operational and technical constraints, by means of pre-industrial prototypes developed on the purpose.

The use of realistic operational environment as validation scenarios as well as industrial prototypes to assess the concept are of utmost importance on the way of the final deployment and decommissioning of the system into operation.

B. High level validation objectives of the iCWP HMI 1) Human Factors

The design and subsequent development of the new ATM system interface shall address human factors issues encompassing the use of new working methods, operational procedures and technology. Operational Usability & other Human Factors issues affecting SESAR iCWP HMI will be assessed to demonstrate the iCWP HMI is usable and ultimately accepted by air traffic controllers (end-users) with no negative safety/human workload effects.

Human factors issues to be analyzed shall include human-system integration issues, roles of automation making ATM decisions vs. human decisions, assessments of roles and responsibility in the new team organization. The evaluation will be carried out mainly by means of Real-Time-Human-in-the-loop simulation where key performance indicators like workload, situation awareness, etc. are carefully observed and, wherever possible, measured. As an example concerning Trajectory management tools, a specific set of validation exercises may include HF aspects and HMI design issues like:

• how to manage and revise a RBT (Multi Sector Planner, data-link functions, synchronization of data-link and voice);

• situational awareness in trajectory management operations;

• smoothing flows of traffic and de-conflicting flights (multi-sector/multi-unit environment, based on new roles for tactical and planning controllers);

• ASAS spacing and ASAS cooperative separation management (fallback options, visualization of new ACAS modes and functionality, ASAS operations in segregated airspace and in a mixed separation mode environment);

• evaluation of the effectiveness of safety nets (impact on the mental image, capacity for providing separation, information overload, presentation of warnings);

• automated assistance to the controller (conflict identification, early detection of potential conflicts; monitoring aids, providing conflict resolutions, system monitoring of trajectories/route deviation).


2) SafetyAny changes to operational systems need to be assessed for

their safety relevance. Systematic safety assessment is the basis for providing assurance that the new or modified air traffic management system is safe for its intended operational objectives and environment. The iCWP HMI Safety assessment intended to be carried out within the project 5.9 will address the three types of system components: people, procedure and equipment. Safety analysis will be conducted and documented to ensure that due consideration is given to all engineering and operational aspects. Selected risk mitigation means shall be validated in both nominal and non-nominal conditions. The results and conclusions of the safety assessment process shall be documented and this documentation maintained throughout the life of the programme to support building of the overall SESAR Safety Case aimed to demonstrate the system is safe for operational use. To assess that the iCWP HMI as finally developed is indeed acceptably safe, Safety analysis within 05.09 is aimed to provide safety feedback to the development process but also detailed description of hazard identification, hazard combination and risk evaluation.

C. Low level validation objectives of the iCWP HMI The SESAR project 05.09 has scheduled a number of

Validation exercises aimed to feed the iterative process of requirements refinement during all stages of project development. In order to assure a general consistency of exercise output, specific low level validation objectives have been elicited to assess:

• impact of iCWP design on controller workload distribution over the shift

• impact of iCWP on controller workload distribution between sector team members

• the extent on which iCWP supports controllers in recovery / contingency situations

• the extent on which the iCWP support controllers in building and retain a short term traffic picture

• the extent on which the iCWP support controllers in building and retain a medium/long term traffic picture

• provision of adequate awareness to controller about the enabled/disabled status of automated functions

• readability and meaningfulness of textual information displayed by the iCWP

• readability and meaningfulness of graphic objects, symbols and visual representations in the iCWP

• consistency and completeness of data displayed by iCWP

• timeliness and prioritization of data displayed by iCWP

• adequacy of information/data sorting from the system

• reachability and intuitiveness of commands on HMI objects

• adequacy of feedbacks of commands/actions on HMI objects

• adequacy of number and sequence of actions on graphic objects needed to accomplish a control task s

• adequacy of iCWP with respect to working methods to be applied accomplishing control tasks

• the extent on which iCWP reduces memory and recall efforts

• the extent on which iCWP reduces reasoning and decision making efforts

• the extent on which iCWP supports controllers trust and confidence in the system

• the extent on which the transition to iCWP might be adverse for controllers

Afterwards, for each specific exercise, applicable objectives are identified and related indicators and metrics are detailed. Following table is going to illustrate metrics and indicators for a V2 validation exercise to be held in October 2011.

Validation objective Indicators Metrics To assess the effect of iCWP on controller workload distribution between teams of adjacent sectors

Workload sharing between adjacent sectors

Difference of subjectively assessed workload between teams of adjacent sectors

To assess the extent on which the iCWP support controllers in building and retain a short term traffic picture

Self assessed situational awareness Situational awareness level judged by the SME

Short term situational awareness (5 minutes)

Conflicts detected by the system Number of displayed STCA alerts To assess the extent on which the iCWP support controllers in building and retain a medium/long term traffic picture

Self assessed situational awareness Situational awareness level judged by the SME

Short term situational awareness (5 minutes)

Deviations detected by the system Number of Deviation alerts displayed To assess if the iCWP provides adequate awareness to controller about the enabled/disabled status of automated

Need of checking check the status of automated functions

Number of times the controllers check the status of automated functions

Acceptability of automated functions Ranking of subjective appreciation of


Validation objective Indicators Metrics functions control means automated functions control means To assess if the iCWP provides adequate awareness to controller about potential problems in medium / long term due to its control decisions (what if / probe mode) error prevention

Clearances sent after a potential problems is displayed

Number of clearances triggered by a displayed potential problem

Coordination made after a potential problems is displayed

Number of coordination actions triggered by a displayed potential problem

Acceptability of display mean of potential problems

Ranking of subjective appreciation of automated functions control means

To assess the readability and meaningfulness of textual information displayed by the the iCWP

Fonts readability Ranking of subjective appreciation of font type Ranking of subjective appreciation of font dimension Ranking of subjective appreciation of font colors

Abbreviations and acronyms clearness Ranking of subjective appreciation of used abbreviation and acronyms

Information density (not overcrowding or poorly textual information displayed)

Ranking of subjective appreciation of quantity of textual information displayed

To assess the readability and meaningfulness of graphic objects, symbols and visual representations in the iCWP

Clearness of information displayed through symbols

Ranking of subjective appreciation of information displayed by newly introduced symbols in HMI

Appropriateness of layout of fields (and textual information) in graphic objects

Ranking of subjective appreciation of fields layout for newly introduced symbols in HMI

Proper association between the type of graphic object and the type of displayed information

Ranking of subjective appreciation of graphic object chosen for providing newly (or differently) displayed information

To assess consistency and completeness of data displayed by iCWP

Acceptability of combination of information coming from different sources (the source combination doesn’t produce unwanted information noise)

Ranking of subjective appreciation of sources integration

Tasks triggered after displaying of data sourced from newly integrated source

Number of tasks triggered by information coming from a newly integrated source

To assess timeliness and prioritization of data displayed by iCWP

Acceptability of time prioritization of actions suggested by automated tools

Number of tasks carried out according the sequence suggested by the system Ranking of subjective appreciation of prioritization proposed by the system

Acceptability of severity prioritization of events displayed by automated tools

Ranking of subjective appreciation of severity prioritization proposed by the system

To assess the adequacy of information/data sorting from the system (iCWP)

Acceptability of effort spent for data finding

Ranking of subjective appreciation towards length of path for finding wanted data

Consistency of status/presentation of all graphic objects concerned by sorted information/data

Ranking of subjective appreciation toward correlated objects presentation (e.g. when a flight in a list is highlighted the corresponding label is highlighted too)

To assess the reachability and intuitiveness of commands on HMI objects

Acceptability of effort spent for new objects handling

Ranking of subjective appreciation towards interaction with newly introduced HMI objects

Presence of shortcuts for commands Ranking of subjective appreciation towards shortcuts

Standard behaviour of HMI objects Ranking of subjective appreciation towards HMI objects behaviour


Validation objective Indicators Metrics To assess the adequacy of feedbacks of commands/actions on HMI objects

Extent of confidence in successful accomplishment of given commands on new objects

Ranking of subjective appreciation towards new objects response to given commands

To assess the adequacy of number and sequence of actions on graphic objects needed to accomplish control tasks

Acceptability of automated updates of new objects/data

Ranking of subjective appreciation of automated updates

Acceptability of confirmation commands needed when using an object/field

Ranking of subjective appreciation of confirmation commands needed when using an object/field (also in terms of not redundancy of confirmation requested)

Acceptability of actions needed for reaching / activating the intended graphic object

Ranking of subjective appreciation of path needed for reaching / activating the intended graphic object

To assess the adequacy of iCWP with respect to working methods to be applied accomplishing control tasks

Acceptability of • Electronic coordination • Deviation Alerts (Lateral and

Vertical) • SNET alerts • ASPA CPDLC • AMAN + CTA

Ranking of subjective appreciation of • STCA alarms • MSAW warnings • ASAS-S&M CPDLC clearances • AMAN advisories + CTA

To assess the extent on which iCWP reduces memory and recall efforts

Time/effort needed for searching information within the HMI (the system)

Subjective assessment of time/effort needed for data searching

To assess the extent on which iCWP reduces reasoning and decision making efforts

Overall workload in a shift/session Subjective assessment of workload Absence/reduction of traffic peaks Steady number of flight per time bit

during the shift To assess the extent on which the transition to iCWP might be adverse for controllers

Time needed for training Self-assessment of errors in new objects use

Overall acceptability of introduced automation

Subjective appreciation towards potentialities of single tool Subjective appreciation towards potentialities of all newly introduced tools

IV. FORTHCOMING VALIDATION STEPS

Despite controllers in the loop activities have not started yet (at the time this paper is written), the intended validation methodology can be considered rather consolidated and ready to be applied. At present, V&V needs (i.e. validation platform capabilities and related measuring tools) are under development to address Step 1 - V3 purposes. Preparatory work is already in place and well progressing for V2 as well. Step 1 validation exercises will be completed and fully documented by mid-2012.

REFERENCES

[1] SJU, “Requirements and V&V Data Structures and Writing Guidelines”, Edition date 01/12/2010, Edition number 01.00.00

[2] SJU, Validation and Verification Strategy, Edition date 02/06/2009, Edition number 01.00.00

[3] SJU, Operational Focus Area Programme Guidance, Edition date 08/12/2010, Edition number 01.01.00

Claudio Vaccaro graduated in Navigation (Scienze Nautiche) in 1993, with a specialization in Air Traffic Management and Surveillance at Istituto Universitario Navale, Napoli (Italy), developing an experimental thesis on Flight Mechanics. From 1994 to 1997 he had been teacher of “Air Traffic Control and Aeronautical Communications” at the Aeronautical Technical Institute in Rome. In 1997 Claudio joined SICTA, where he is currently responsible of the Unit “Operational Concept and Studies”. He has a multi-year experience in setting up and leading National and International teams focusing on the definition and Validation of innovative Operational Concept in the Air Traffic Management domain. At present, he is the Project manager of SESAR P05.09 project (Usability Requirements and Human Factors Aspects for the Controller Working Position).

Gabriella Duca is post-doc fellow at LEAS, the Laboratory of Applied and Experimental Ergonomics of University Federico II, in Naples and registered at CREE as European certified ergonomist (EurErg) since 2003. She works as academic researcher and professional consultant in the field of HF/ergonomics for safety critical contexts, focusing on user-centred design of industrial systems, workplaces and HMIs. She has carried out research projects in aeronautical, pharmaceutical and chemical work environments. Currently she is HF consultant for the SESAR P05.09 work package.


A new concept for ATM Service Supervision for the new SESAR Enterprise

ArchitectureG.Carrozza

SESM s.c.a.r.l - Via Circumvallazione Esterna di Napoli, 80014 Giugliano in Campania – Naples,Italy

[email protected]

H.Souami Thales Air Systems, Parc Tertiaire SILIC, 3 Avenue Charles

Lindberg, BP 20351, 94628 RUNGIS Cedex, France [email protected]

S.Straub DFS Deutsche Flugsicherung GmbH, Am DFS

Campus 7, D-63225 Langen, Germany [email protected]

Abstract—The SESAR Definition Phase stated the need for a service oriented approach to meet the European scope interoperability in the ATM domain, aiming at striking out fragmentation among systems and countries, and at providing the holistic view of a single Pan-European ATM network whose services must be driven by stakeholders’ needs. The goal of 10.1.9 (for en-route) and 12.1.9 (for airport) SESAR projects is to define a novel supervision approach, able to tackle the challenge of such a paradigm revolution, and to take into account the Quality of Services and dependability constraints that this will introduce into the next generation systems.

Keywords-component; supervision;SOA

I. MOTIVATION

The SESAR Definition Phase stated the need for a service oriented approach to meet the ATM Target Concept, i.e. the European scope interoperability in the ATM domain, aiming at striking out fragmentation among systems and countries, and at providing the holistic view of a single Pan-European ATM network whose services must be driven by stakeholders’ needs. From the development perspective, a Service Oriented Architecture (SOA) is the best candidate for enabling the ATM Target Concept, which will allow the orchestration of distributed resources and capabilities, even controlled by different ownership domains. SOA is in charge of decoupling ATM services from the underlying technologies and systems, as well as from the physical items to be deployed.

Information sharing and cooperation among differentsystems are intended to be the pillars of the next generation of ATM systems to achieve the ATM Target Concept. In fact, the foreseen increase of air transport demand, which should be three-fold by 2020 if compared to today’s traffic, asks for an improvement of existing systems and infrastructures, resource planning and management processes, as well as of supervision systems which have to fit the new service oriented perspective in order to ensure the required Quality of Service (QoS) levels. This is the goal of 10.1.9 (for en-route) and 12.1.9 (for airport) SESAR projects aiming at defining the supervision

requirements, able to tackle the challenge of such a paradigm shift. Dependability and availability constraints characterizing the ATM scenario, where systems are generally distributed and made up of several interacting components/services thus complicating the task of system health monitoring and control, makes crucial the task of supervision. Actually, supervision is system management, i.e., the ability of controlling the status of the monitored systems and of starting recovery/reconfiguration actions to prevent or react to QoS degradations due to anomalous and unexpected events. Also known as dependability threats, these events i.e., faults, errors and failures (according to the definitions in [1]), can propagate among components and manifest at user level with consequences that can even be fatal in terms of business damages and human life loss. The more complex the system the harder to detect the error and locate the real cause of a failure, especially if it is not located in the same system component or interface where it manifested. Diagnosis (fault location) aims to locate the root cause (fault) of a failure, once it has been detected, in order to undertake the most proper recovery action. In a general perspective, supervision systems based on diagnosis can be thought as made up of (i) a failure detector, aiming to detect the presence of an error and to trigger alarms, (ii) a fault locator, aiming to go back to the root cause of the error/failure (i.e. the fault) and (iii) a recovery block, aiming to select and trigger either automatic or manual recovery/reconfiguration. To the state of the art, a few proposed strategies reveal to be effective to manage a set of interconnected, mainly homogeneous, nodes through centralized fault detection and avoidance protocols. However, traditional solutions proposed so far have to be rethought for use in a Service Oriented Architecture, for which the presence of reusable, independent, and heterogeneous services, as well as the facility to compose them at run-time, rise new challenging issues:

• Which are the actual dependability threats (e.g. what to claim a failure)? In the service oriented paradigm, performance are a major concern as services have to


be delivered with a required quality level. Hence, performance and quality metrics shall be defined todiscriminate faults; • Which information should be monitored to diagnose the failures? Service oriented applications often comprise several components distributed even across different countries. For example, network monitoring may be crucial to go back to the fault location (e.g., the failure of a link or the crash of the remote node).

• How to contain faults in face of propagation and coupling among services? As failures can propagate among services, failure modes may be not independent. Complex services often come from the composition of simpler services, hence faults located into a single module may degenerate into larger failures due to faults cascade and propagation. This means that (i) failure modes of different services may depend on each other and (ii) larger problems may come from multiple root causes. For example, if services get composed dynamically, it is very unlikely to anticipate dependencies and fault propagation paths ahead of runtime.

To the best of our knowledge, the above challenges still represent open research needs in the field of dependable Service Oriented Computing (SOC) systems. Since actual systems addressing these issues have not been developed yet, the projects aim to push the state of the art by proposing a novel approach tailored for the SESAR next generation of service oriented systems. A general and flexible approach is envisaged, in which both automatic knowledge and human experience about the system will be merged in order to enable automatic and, if necessary, manual system recovery and reconfiguration.

II. THE PROPOSED APPROACH

An high level architectural view of the proposed supervision approach is sketched in Figure1. Since most of the anomalous events (i.e., the faults) occurring within the overall system (e.g., the crash of a remote node) propagate to the service level interface, we assume failures to manifest in the form of service level degradation. For this reason, a Service Level Monitor is in charge of detecting whether service levels got compromised or they are likely to be violated, and of triggering alarms. To this aim quality metrics have to be defined, along with reference values for all the provided services, which will be managed (i.e., stored and potentially updated) by the QoS Policy Manager. Once an alarm has been detected, the Fault Locator has to pinpoint the root cause of the manifested failure. Knowledge about the faults that can actually affect the monitored service and/or system will be coded into a fault library. Of course, it is unlikely to be exhaustive due to the presence of runtime failures, which cannot be predicted during preoperational phases of the system, as well as to propagation phenomena.

Figure 1: High level supervision system architecture

For this reason, updating and feedback mechanisms aiming to continuously increase this knowledge could be helpful. Once the root cause has been finally pinpointed, recovery can start. If location went through, it will be possible to start the most proper recovery action for the particular fault that occurred. This means a significant reduction of recovery time and costs, e.g., it is not needed to reboot a node if the failure came from a link fault. The real benefit of this approach is twofold:

• It is general and flexible enough to be used in several ATM configurations since knowledge organization and management strategies do not depend on the particular ATC/airport system configuration (rather, it will be customizable according to customers’ needs);

• It can be improved over time, by leveraging human knowledge and experience. Indeed, it will allow to enlarge the supervision knowledge by recording recovery actions performed by human operators when automatic recovery is not suitable. Indeed, off-line validated recovery actions can be leveraged to feedautomatic recovery level.

III. IMPLEMENTING TECHNICAL SUPERVISION IN SESAR

In order to implement the proposed approach and to realize a supervision system in charge of meeting actual requirements, the identification of the services to be monitored as well as of their QoS expectation is paramount. In the framework of SESAR, and bearing in mind that 10.1.9 (for en-route) and 12.1.9 (for airport) projects are in charge of technical supervision at domain level, the service orientation approach described above can be implemented assuming that:

• Services to be monitored are the ones provided by 3rd level projects;

• QoS expectations are exposed by 3rd level projects for each service;

• Technical supervision service is going to be provided by 10.1.9 and 12.1.9 to 3rd level projectsthat will subscribe.


Going deeper into details, the implementation of such an approach requires to clearly define what is meant by:

• Services (S), i.e, its lifecycle, behavioural and technological description, its configuration and deployment issues. These will be defined in terms of models and will have to take into account, for instance, the state of a given service in a given instant (e.g., a service can be accessible by a consumer if it is deployed);

• Providers / Consumers (P / C), in terms of which project is in charge of providing/consuming inputs;

• SLA (Service Level Agreement), i.e., the specification by the project providing the service of their terms of use for making the service available(including dependability commitments) and the publication of these terms to potential consumer projects;

• Communication protocols (C), i.e., how to manage the initial request for an SLA based on a publishedtemplate/offer, the granting of the request enabling a SLA being done by Ps and Cs, the access to the service under the agreed conditions.

Hence, 3rd level projects act as consumers of the technical supervision services provided by 10.1.9 and 12.1.9. On the other hand, they will play the role of P/C with respect to other peer level projects with which they are called to interoperate. Actions taken by the technical supervision system in the form of recovery services (see recovery block in Figure 1) will consist of both proactive and reactive actions, such as:

• Changes in the available resources for a given service (e.g., negotiate additional SLAs to providefurther resources to the provider);

• Management of the demand on the service to prevent any further increase in the level of service commitments for an overloaded provider.

• Restart of a failed service

These actions can in principle be taken automatically, but human intervention may be required in some cases. For this reason the technical supervision system will also provide administration HMIs and decision support facilities giving operators direct access to the service information and monitoring data. Human operator will be kept informed about the actions that are needed, and will have then to decide whether and if so how to act.

In order to define what are the requirements in terms of technical supervision exhibited by the projects, and to support the definition of SLAs among the involved parts, a Requirements Definition Template and the structure of the SLA have been defined in the context of the 10.1.9 and 12.1.9 projects. These are aimed to support supervision consumers into the definition of a customized technical supervision service, tailored for their specific needs. Although there does not exist an exhaustive list of services within SESAR yet, it can be helpful to start having an

overall picture of what are the needs and the links among several projects. Figure 2 shows the defined template for SLA. At the time of this writing, the process of requirements definition according to the provided template is ongoing. Its output will bring to the definition of an overall set of requirements, as well as to the development of the supervision system itself. For any further details refer to [2].

Figure 2:SLA TEMPLATE

IV. EXPECTED INFLUENCE ON ATM PERFORMANCE

The SESAR Masterplan defines a performance framework of Key Performance Areas with clear objectives, indicators and targets ([3]). The implementation of the supervision concept presented here aims to bring significant contributions to the accomplishment of the following performance goals:

• Safety: the early detection of quality of service degradation enables immediate action to avoid the failure of safety-critical services. KPI: accident probability per operation or flight hour (+++)

• Cost Effectiveness: reduced costs due to efficient monitoring, control and recovery of systems.

• Reduced costs due to efficient monitoring, control and recovery of systems. KPI: availability of each supervised system (+++)

• Reduced delays from system downtime reduce the associated costs. KPI: operational costs on ATC systems supervisory / operational costs saved due to failures (+++) KPI: average cost per flight (+)

• Capacity: timely identification and treatment of errors prevent them to degenerate into failures that might affect the system operation considerably. Then, required levels will be supported with less impact due to technical failures. KPI: annual/daily/hourly number of IFR flights (+)


• Efficiency: reduced workload for technical personnel in terms of system operation check-up and service quality inspection, system control and recovery effort. This especially applies to situations where service providing systems are spatial separated from the user, e.g., services used in a Remote Tower. KPI: availability of each supervised system (+++)

• Flexibility: service surveillance is independent ofthe service-providing system and therefore more flexible if systems need to be switched for maintenance or completely replaced. KPI: number of supervised systems/parameters (++) KPI: average delay of delayed flights (+)

• Predictability: measuring degradation of service quality allows predicting availability/loss of service (in some cases). KPI: time until system is restored after a failure (+++)

• Interoperability: surveillance of interdependent operative systems enables the detection of error propagation and, consequently, assures interoperability of these systems. KPI: level of ATM service seamlessness to the user (+).

V. CONCLUSIONS

The paper presents a novel and ambitious service supervision and recovery approach, which is considered to be the coherent approach to guarantee the required QoS of ATM Services. The efficient and reasonable implementation of service surveillance is best supported by providing the appropriate framework, so that the actual implementation can concentrate on the details of the services to be monitored. Using one framework for all services to be monitored allows assessing error propagation even in complex and critical situations. The actual development of such a challenging idea is envisaged by10.1.9 and 12.1.9 SESAR projects.

REFERENCES

[1] A. Avizienis, J.C. Laprie, B. Randell, and C. Landwehr. Basic Concepts and Taxonomy of Dependable and Secure Computing. IEEE Trans. on Dependable and Secure Computing, 1(1):11–33, 2004.

[2] 10.1.9 Project. D02.10.01.09 requirements definition template report. 2011.

[3] SESAR JU. Air transport framework - the performance target.


ADS-B integration in the SESAR surface surveillance architecture

Andrés Soto, Pedro Merino, Jorge Valle SESAR Programme. International ATM & Airports

Indra Sistemas. S.A Torrejón de Ardoz, Madrid

[email protected], [email protected], [email protected]

Abstract— Assuming the current baseline architecture of the Surveillance Function presented in the SESAR context for the future aerodrome ATC systems, this paper proposes different strategies for ADS-B report integration, as being in development and implementation in the future SESAR-compliant products to be provided by Indra. For one of the proposed strategies, an evaluation will be performed based on scenarios focused on Barajas Airport.

Keywords- SESAR; architecture; surface surveillance; ADS-B; integration;

I. INTRODUCTION

A. SESAR paradigm The new environment based on the future SESAR

paradigm pretends to change the Air Traffic Control operator (ATCo) decision making, nowadays more based on a reactive behavior, into a more proactive one. Besides, the ATM Target Concept described in the SESAR Definition Phase [1] is developed around the 4D Trajectory concept whose aim is not only to improve the current knowledge about the aircraft location but also to improve the future one. The latter will imply that conflicts can be envisaged with enough margin to propose more efficient, cost-effective and greener solutions than in the case in which tactical controller interventions are needed.

B. SESAR at the airport Other important matter about SESAR is that its ultimate

aim is to achieve the Integrated Airport Operations. This fact is based on the extension of the 4D Trajectory management which will include the movements around the airport.

The effect of this extension will be the advanced knowledge about the airport movements which will be reflected in a reduced impact of ATCo actions on the rest of the mobiles at the airport surface ensuring a degree of strategic de-conflicting while minimizing holding and ground queues.

Thus, it is expected that all mobile movements in the manoeuvring area will have their own assigned route which will be supervised by the airport Conformance Monitoring function in order to check that they are behaving as expected. In this situation, it is important to point out that the information

coming from the Surveillance Function is crucial as it provides the current situational awareness picture to be compared with the predicted airport situation.

The same happens with the Safety Nets. In this case, the main problem is that they need very accurate surveillance information in order to calculate the possible conflicts. A recognized issue for the implementation of the A-SMGCS Control function is the false alarms that interfere with controller operations [2]. This is also related to the definition of the A-SMGCS Implementation Levels which link the evolution of the concept to the available procedures and technology performances. The aim in SESAR is to consolidate A-SMGCS Level II and then move towards level III and IV.

In order to solve these issues, there are different possible solutions:

• The introduction of new sensors such as ADS-B or multilateration;

• The improvement of the current ones;

• The improvement of the data fusion.

This paper is going to focus on the first option due to the fact that SESAR considers that the ADS-B is the cornerstone for moving to the following A-SMGCS Levels as it provides more accurate and frequent surveillance information than current sensors, which improves the situational awareness picture presented to the tower ATCo. Besides, it can easily provide a seamless hand-over between the TMA and the Tower control helping in track continuity.

Firstly, the paper presents the baseline architecture of the Surface Surveillance Function which has been defined in the SESAR framework as part of the Architecture Assessment task of project 12.3.1 "Improved surveillance for surface management" [3]. This architecture is described in order to present those elements that can be of interest or affected by the introduction of the different alternatives for integrating ADS-B reports.

Secondly, an overview of known issues of ADS-B is presented. The objective is to introduce the problems to be tackled as it is envisaged that ADS-B will be the key actor of the evolution of the airport surface surveillance.


Next, the different operational scenarios that have been considered to integrate the ADS-B reports are detailed. The information provided is a summary of the corresponding technology reports developed by Indra in the framework of the 12.3.1 project.

Section V presents the results of a simulation of one of the operational scenarios presented in the previous section. This simulation is based on Barajas Airport where several routes along the airport are simulated.

Lastly, the results obtained by simulation will be shown. Thus, a precision analysis is provided in order to compare the performance of a surveillance function based on just a SMR sensor versus one based on ADS-B data.

II. SESAR SURFACE SURVEILLANCE ARCHITECTURE

This section provides the baseline architecture of the Surface Surveillance Function which has been defined in the SESAR framework. Thus, the architecture is described in detail in [3]. However, for the sake of coherence, an overview of the functions has been provided in order to present those elements that can be of interest or affected by the introduction of the different alternatives for integrating ADS-B reports. The logical system architecture view is provided in the figure below:

Figure 1. SESAR baseline architecture for the Surface Surveillance Function

The main internal functions identified as part of the Surface Surveillance Function are:

• External Surveillance Tracks Preprocessing: It is the function capable of acquiring and preprocessing External Surveillance Tracks which are the ones coming from other collaborating Air or Surface Surveillance Functions.

• Acquisition and Data Preprocessing: It is an intermediary in the reception of data coming from all sensors (radar, MLAT, ADS-B, etc.). It performs the conversion both of media and format, when necessary, and could include coordinate transformations, sensor systematic error (bias) correction, measurement completion, etc.

• Sensor Status Data Processing: This function is able of acquiring and processing sensor status data, and maintaining a sensor status list with the current status information for each sensor feeding the Surveillance Function.

• Airport Layout Processing: This function is in charge of interpreting the airport layout adaptation information, merging it with potential Operational Supervision information related to airport layout (dynamic airport configuration information), and providing this data to be exploited by Acquisition and Data Preprocessing, External Surveillance Tracks Processing and Multisensor Data Fusion functions.

• Airport Flight Plan Processing: This function exploits the information coming from correlated Flight Plans in order to improve target identification and to adapt Acquisition and Data Preprocessing and Multisensor Data Fusion functions to Flight Plans.

• Surveillance Environment Assessment: This function is in charge of processing the sensor status and quality (summarized in the sensor status list described in previous Sensor Status Data Processing function) and potentially deriving some additional quality assessments of the measurement process for each sensor (e.g. covariance matrix, probabilities of detection, bad measurement areas, etc.) which can be used to improve its associated sensor model.

• Surveillance Environment Adaptation: This function adapts Acquisition and Data Preprocessing and Multisensor Data Fusion functions to surveillance sensors status and quality. The different sources of information are:

o Static data, from the Adaptation system;

o Dynamic data provided by Technical Supervision systems;

o Surveillance Environment Assessment output.

This adaptation is performed by precluding the use of a given malfunctioning data source and also adapting measurement assumed quality to current situation.

• Multisensor Data Fusion: This function receives measures (plots) and tracks from the Surface Surveillance Acquisition and Data Processing coming from all available sensors; monosensor trackers; and potentially from cooperating Surveillance functions (preprocessed by the External Surveillance Tracks Preprocessing function) in order to generate Surface Surveillance tracks, which


include an assessment of their quality, describing the whole Surface Situational Awareness Picture. It can also be divided into three subfunctions:

o Multitarget Multisensor Tracker;

o Sensor Calibration;

o Classification/Identification.

• Output Data Server: This function is in charge of the distribution of the Surface Situational Awareness Picture, providing system track data to internal A-SMGCS modules (Control, Routing or Guidance), to Controller Working Position for air situational awareness display elaboration or to external systems (i.e. other external Surveillance Functions).

• Data Recording: This function, which has access to the internal status of the Surveillance Function and makes use of the services provided by Data Recording and Playback infrastructure, is in charge of recording Surveillance related information.

• Internal Supervision: This function is responsible for:

o Monitoring and displaying the status of the Surveillance Function;

o Performing the start-up, shutdown and restart of the Surveillance Function and its components;

o Allowing the operator to modify Variable System Parameters (VSP) that customize the system's technical characteristics;

o Generating alerts and displaying diagnostic information in the event of faults in the system;

o Exporting status, alerts, and diagnostic data to External Supervision;

o Allowing the operator to re-configure the systems to maintain agreed levels of service provision as appropriate;

o Managing changes in the airport layout due to Operational Supervisor actions.

Although not explicitly mentioned in any of the previous descriptions, the Internal Supervision controls the behaviour of all of them.

The focus will be place on the functions that allow adapting to the different solutions for integrating ADS-B. These functions are mainly the Adquisition and Data Preprocessing, the Multisensor Data Fusion, the Surveillance Environment Assessment and Adaptation.

III. ADS-B KNOWN ISSUES

The next sub-sections present the known issues concerning ADS-B.

A. Equipage For complete coverage, all potential targets have to be

equipped with ADS-B capable transponders. As the technology has been introduced gradually, there is a transition period in which the full benefit of pure ADS-B cannot be realized.

B. Time synchronization This is an already known problem which it is mainly related

to airborne aircraft due to their high speed. On the surface, this problem is not remarkable [5].

C. Coordinate transformation Depending on the algorithms used for transforming

Latitude and Longitude into the stereographic plane centred on the data fusion centre, some error can be introduced. It is important to estimate whether this error is significant in order to include it in the sensor measurement model.

D. Biases The main bias that has to be addressed while fusing data

with ADS-B report information is the one related to the position of the GPS antenna (assuming that the Time bias is negligible). The position reported by the transmitting ADS-B device is the location of the A/V’s ADS-B Position Reference Point, if the POA (Position offset applied) field is set to one, and the GPS antenna otherwise [6][8].

E. Transmission problems This can be one of the biggest problems concerning the

integration of ADS-B in the airport Surveillance Function. There are several cause which justify the disappearance of ADS-B data [9][10], namely:

• Reflections;

• Occlusions by buildings and other A/V;

• Frequency shared with other A/C and systems (SSR, DME, etc).

This fact implies that sometimes the system has to deal with the absence of ADS-B reports which can be in part solve by coasting. However this technique implies a trade-off between the probability of detection and the accuracy of the provided position [9].

F. Integrity As any secondary surveillance technology, successful

surveillance requires the cooperation of the targets. However, pure ADS-B not only relies on a functional transponder, but also on the integrity of the aircraft navigation system. If this fails, the aircraft will not be able to broadcast its position, or worse, it may broadcast invalid positions which satisfy the CRC code which protects from undetected errors. On the other hand, GPS is the only source of information for the aircraft navigation system which represents a unique failure point. However this problem will be solved once Galileo is up and running.


Using pure ADS-B, there is no way to verify the position or even presence of an aircraft. This is the reason why some kind of mitigation mean has to be put in place.

Another important matter is that there are integrity parameters which provide information concerning the reported position integrity such as the already mentioned NIC and SIL [4] [7]. This information has to be exploited as much as possible by the Surveillance Function in order to keep the good measurements and discard the not reliable ones.

Besides, continuous self-monitoring and testing enables the timely detection of problems with the sensor, and can quickly provoke corresponding reactions by the users of sensor data.

G. Security It is relatively easy to broadcast fake ADS-B messages

simulating non-existent aircraft. This case is broader, but not substantially different in risk from a classical secondary radar transponder reporting a wrong Mode-C altitude; however since ADS-B messages are broadcast, they are available to everybody with the right equipment. Except for regulatory action, there is no way to restrict the availability of aircraft positions.

IV. ADS-B INTEGRATION AND OPERATIONAL SCENARIOS

The initial strategies to be considered are the following:

• Airport surveillance based on ADS-B as primary source of information with build-in integration checking;

• ADS-B as the primary surveillance source of information supported by an SMR or MLAT as the backup solution and the mean of checking the ADS-B data integrity;

• ADS-B as another source of information to be provided to the multi-sensor data fusion.

For each of them, the modifications to the SESAR surveillance functional architecture, the proposed algorithms, the backup solution and the constraints due to the needed sensor equipment will be presented.

A. ADS-B as unique source of surveillance information Stand-alone ADS-B surveillance is envisaged for small size

aerodromes where the traffic load does not justify the investment on a radar-based solution.

However, there are some tests that need to be done to ADS-B data in order to check its integrity. Some of these tests are expected to be performed by the ground station, but in some cases they can be repeated by the surveillance function.

The ones that can be performed by the ground station are the one related to the reception of the signal. Thus, the use of sectorised antennas, time consistency checks and power analyses can perform an initial filtering of the income data.

Thus, the Surveillance Function can perform different tests in order to check if the incoming data is feasible or not. Several of these tests might be similar to the ones performed by the

ground station, but it is important to repeat them as the ground station might also be a failure point. Some examples are:

• Position test: it analyses whether the position reported by the ADS-B ground station is feasible considering times and distances.

• Velocity test: the airport topology constraints on aircraft movements can be used to remove fake or erroneous data.

• Time test: it analyses the age of the measurement.

• Quality indicators: can provide information about the status of the data source and quality of measurements.

Taking into account the Surveillance Function described in section II the following modifications have to be performed.

• Acquisition and Data Preprocessing: this function has to include the tests described above in order to guarantee that the ADS-B data provided to the rest of the functions are good enough to be processed. The correction of the GPS antenna position bias has to be implemented.

• Multisensor Data Fusion: this function has to estimate the position offset of the GPS antenna in case necessary. The Acquisition and Data Preprocessing will use it for correcting this bias from the new incoming measures associated to the corresponding target. Due to the problem related to the disappearance of ADS-B measurement, it is convenient to implement a tracking filter for being able to provide coasted measurements.

• Surveillance Environment Assessment: this function should implement an online evaluation tool in order to update the measurement model of the ADS-B.

In this scenario there are two possibilities: to dispose of one or several ADS-B ground stations. The second case allow to perform a better position test as a multilateration-based position estimate can be obtained and also crosschecks between ground stations can be done. Concerning the first possibility, there is no backup solution apart from the controller. In the second one, the rest of the ground stations will provide it, so the failure of one of them will be transparent for the Surveillance Function.

B. ADS-B as the primary surveillance source of information supported by other sensors This scenario, which is an evolution of the previous one,

can be of interest for medium/high density airports where the number of aircraft is not easy to handle without a surveillance system. The number of movement has to be enough to justify the investment on a radar sensor.

In this case, the surveillance function is fed by different types of sensors which improve the robustness of the system. It is assumed that there are several ADS-B ground stations which are connected to an ADS-B server. The ADS-B is considered as the primary source of surveillance information (because of


its better performances in a normal situation) and will be the one displayed on the controller’s screen. The other surveillance sensors (SMR and MLAT) are just kept for integrity checks and as backup solutions.

In this scenario, the same tests, which were presented in the previous one, have to be performed as they guarantee the integrity of ADS-B data. However, in this case the surveillance function does not rely on just one type of sensor which gives more confidence in the output shown to the controllers.

The upgrades to the architecture are:

• Acquisition and Data Preprocessing: the same as in the previous scenario.

• Multisensor Data Fusion: It has to estimate the GPS antenna position offset. In order to exploit the information coming from the back up surveillance sensors (e.g. SMR or MLAT) the Multisensor Multitarget Tracker implemented in this case should maintain two monosensor tracks per target, one fed by ADS-B data and the other by the backup sensor. Thus, the MSDF can check the coherence and integrity of the ADS-B monosensor track using the other one. This integrity check has to be performed before sending the information to the controller display. If a lost of integrity or a disappearance in ADS-B data is detected, the system smoothly transitions to the output of the backup solution, which is assumed to be less accurate but more stable. So, the MSDF has to implement the logic for performing the soft handover.

• Surveillance Environment Assessment: the same as in the previous scenario. The output of this function can also include some directions to the MSDF (Through the Surveillance Environment Adaptation function) concerning the start of the handover.

In this case the backup solution is provided by the output of the monosensor track based on the data coming from the back up sensors (e.g. SMR or MLAT).

C. ADS-B as another source of information in a multisensor environment This is the most general scenario where there are several

sensors of different kinds which can be fused for improving the output data to be sent to the controller display.

Even though this is the most common configuration that can be found in high density airports, the trend is to move to a scenario similar to the one described in previous section. This will be possible once the main problems of current ADS-B data link are solved. These problems are the dependency with GPS, which will be solved once Galileo is up and running, and the improvement against sudden lost of the data link. A possible solution for the latter is to change the data link technology but this implies a huge investment for the airlines which will only be possible when they are fully convinced of its benefits. However, SESAR is working in this direction.

This scenario includes several ADS-B ground stations, one or more SMR, a MLAT system and can also include camera

systems. All these systems feed the surveillance function in the same conditions. The weight that is given to each of them depends on their measurement models but not in any preference based on the sensor.

Similar to the previous scenario, several tests are performed to check ADS-B integrity but the dependability on this sensor is reduced as there are other sensors which are included in the data fusion.

The proposed changes to the architecture are:

• Acquisition and Data Preprocessing: the same as in the previous scenarios.

• Multisensor Data Fusion: this function has to estimate the GPS antenna position offset. In this case there are two clear options in order to fuse the data coming from the sensors one is based on a centralized architecture in which the system process directly raw plots and the other is based on building monosensor tracks which are then used to update a multisensor one. Depending on the behaviour of the sensor, one approach or the other can be used.

• Surveillance Environment Assessment: the same as in the previous scenario.

In the case of a distributed architecture (monosensor track processing), the backup solution is provided by the output of one of the monosensor tracks that can be computed in parallel to the multisensor one. For a centralized architecture, the disappearance of one of the data sources should not impact the processing only the quality of the output.

V. RESULTS

In order to show the advantages of building the SESAR Surveillance Function based on ADS-B data, a simulated scenario is presented. The scenario is based on the Scenario B which has been defined in the previous section. This scenario is located at Barajas airport and consists of a SMR sensor and an ADS-B server.

Figure 2. Simulated trajectories on Barajas airport

TR1

TR2

SMR1


SMR1 is located in Cartesian coordinates in the following position (-900, -400, 80), which corresponds with the tower located close to Terminal 1. The measurement noise is modeled by the range standard deviation ( R) and by the azimuth one ( ):

• R: 5m

• : 0.15º

In this simulation, two trajectories (TR1 and TR2) are presented. TR1 represents an aircraft which departs from a stand in Terminal 3 and takes off from runway 15R and TR2 represents an aircraft which departs from a stand in Terminal 4S and takes off from runway 36R.

For the sake of simplicity, some of the already presented effects are not taken into account in order to focus on the simulated ones and not being disturbed by the joint effects of the rest of them. Thus, the following assumptions are considered:

• The system receives ADS-B data from an ADS-B server, so the problem with duplicates is not present.

• The ADS-B time bias and the coordinate transformation effect are negligible.

• ADS-B data is based on GPS receiver and never in the INS system of the aircraft.

• The biases of SMR systems have been cancelled.

Thus, ADS-B is the primary surveillance source of information which is supported by an SMR sensor (SMR1). So, there are two independent monosensor tracks. The main one is the one based on ADS-B and the one provided by the SMR is the backup one. The following figure represents the RMS error of both monosensor tracks for TR1 after performing a Monte Carlo simulation of 100 iterations.

Figure 3. RMS error for the two sources of information (SMR and ADS-B)

Assuming that the results for TR1 are representative enough, the integration of ADS-B will considerably improve the quality of the output of the Surveillance Function provided that the already known issues are conveniently addressed. In order to achieve A-SMGCS level II, which implies the 7.5 m at 95% percentile of accuracy, the integration of ADS-B is almost mandatory.

Besides, the output of the SMR sensor guarantees that its performances also comply with the A-SMGCS level II accuracy requirement, so its output can perfectly take over the role of back up surveillance source. For those cases in which a better performance is required, the output is improved by including an IMM filter which exploits the airport layout at the expense of increasing the complexity of the MSDF function.

REFERENCES

[1] SESAR Definition Phase, “Deliverable 3. The ATM Target Concept”, September 2007.

[2] EUROCONTROL, “Definition of A-SMGCS Implementation Levels”, November 2005.

[3] SESAR Development Phase, “12.03.01.D02. Phase1-Architecture design”, Ed. 00.01.00, January 2011.

[4] ICAO, “Doc 9871 - Technical provisions for Mode S services and extended squitter.”, 2008.

[5] G. de Vela et al., “Integration of ADS-B surveillance data in operative multiradar tracking processors”, Information Fusion, 2008 11th International Conference on, 2008, 1-8, 0.1109/ICIF.2008.4632309.

[6] RTCA, “DO-242A, Minimum Aviation System Performance Standards for Automatic Dependent Surveillance Broadcast (ADS-B)”, June 2002.

[7] SC 186 WG-6, “Proposed Revisions to ADS-B MASPS: Integrity and Accuracy Monitoring”, August 2001

[8] J.A. Besada et al., “On-line sensor calibration for airport data fusion”, Radar Conference, Proceedings of the IEEE, 2004.

[9] Luca Saini et al., “1090ES ADS-B Surveillance for vehicle tracking-Performance Results”, ESAVS 2007, Bonn, Germany, March 2007.

[10] Paul Askew, NATS, “Evaluation of ADS-B at Heathrow for EUROCONTROL ADS Programme Report”, June 2002.

[11] A. Smith et al., “System-Wide ADS-B Back-Up and Validation”, presented at the Integrated Communications, Navigation, & Surveillance Conference, Hyatt Regency Baltimore, Baltimore, Maryland, 2006.

[12] ICAO, “Guidance Material on Issues to be Considered in ATC Multi-sensor Fusion Processing Including the Integration of ADS-B Data”, September 2008.

[13] J.D. Powell, C. Jennings, y W. Holforty, “Use of ADS-B and perspective displays to enhance airport capacity”, in Digital Avionics Systems Conference, 2005. DASC 2005. The 24th, vol. 1, 2005, 4.D.4-4.1-9 Vol. 1, 10.1109/DASC.2005.

[14] Campbell, S.D.; Grappel, R.D.; Flavin, J.M., “Multi-sensor processing for aircraft surveillance in mixed radar/ADS-B environments”, in Digital Communications - Enhanced Surveillance of Aircraft and Vehicles,2008. TIWDC/ESAV 2008. Tyrrhenian International Workshop on,September 2008, 10.1109/TIWDC.2008.4649022.


Future mobile satellite communicationProject 15.2.6: The “feeder link” to the ESA Iris Programme

Pierpaolo Tavernise Thales Alenia Space Italia

Via Saccomuro 24 00131 Rome - Italy [email protected]

Abstract— SESAR Concept of Operations set out in the SESAR Definition Phase (2006-2008) addressed the issue of the inadequacy of the current ATM communication link identifying new communication technologies that will enable improved voice and data exchanges due to the estimated increase of the flight traffic in the future. Currently SESAR WP15 addresses the non-avionic CNS technologies development and validation comprising the definition of the future mobile datalink systems to serve communication services. Within WP15, the overall objective of the Project 15.2.6 “Future mobile satellite communication” is to define the requirements for the future Satellite Communication System developed in the ESA Iris Programme, to perform the complementary activities of validation and to support the standardization of the new satellite link. The present paper describes the status of P15.2.6 on going activities.

Keywords-SESAR; satellite; ATM; communication; SATCOM; ESA; Iris

I. INTRODUCTION

SESAR Concept of Operations [1] and the ATM Master Plan [4], endorsed by the EU Transport Council, indicates the following solutions to meet the new long-term communication requirements:

• increase the use of digital technology and protocols. Data link in the future will become the primary means ofcommunications [2] and voice while will not be able to be used as back back-up will remain available for emergency communications [6],

• implement terrestrial and satellite based new data links to complement VDL2 over high-density continental areas[4],

• provide satellite communication as the primary mean over oceanic, remote and polar areas [5],

• fully integrate the terrestrial and satellite networks realizing a dual link system [3].

The new ATM satellite data link will moreover provide the advantage to offer complementarities to the terrestrial links in terms of coverage and infrastructure and radio spectrum diversity.

II. THE MULTI LINK OPERATIONAL CONCEPT

Among the SESAR WP15 (Non avionic CNS Aspects) projects, the project P15.2.4 (Future Mobile Data Link System Definition) is a key project aiming to lead the definition of the future communication system meeting the future requirements for all phases of flight. In particular SESAR Project 15.2.4 (P15.2.4) is in charge of the definition of the overall system aspects of the Future Communications Infrastructure (FCI) to be developed under the SESAR Programme.

A new specific functionality has been identified as one of the main concepts of the FCI with the objective to provide a robust and high availability connectivity adapted to safety critical application services. This specific functionality is identified as the “Multilink concept”. The multilink concept should enable the seamless use of different technologies supporting the future ATM concept.

The SESAR ATM concept introduces new ATM services that are demanding in data exchanges (latency, capacity, availability, …) as

o 4D Trajectories Management, CDM

o Meteo info, SWIM, …

SESAR introduced initially the Dual link concept [3] as a way to limit the impact of events such as Loss of Service. The contemporary availability and usage of multiple new data links in a transparent way is the object of the multilink operational concept (ML OC) defined in the P15.2.4 [10].

The Multilink Concept is involving three new communications links:

• AeroMACS - a ground-based, high-capacity, airport surface datalink system for the aeronautical mobile airport communications

• LDACS - a ground-based datalink system for continental airspace in L-band for digital aeronautical communications

• SATCOM - a satellite-based datalink system for the oceanic, remote and continental environments


SESAR P15.2.4 is focused on the overall FCI and in particular on the LDACS. SESAR Project 15.2.6 (P15.2.6) is focused on the activities related to the new satellite datalink (SATCOM). Following the SESAR vision, the Future Communication Infrastructure (FCI) will be a system of systems integrating existing communication sub networks (VDL2) as well as the new communications sub networks

Figure 1: SESAR FCI Multilink Concept

Currently, there are two satellite communications system for ATM services: the INMARSAT3 and IRIDIUM systems. However, the performance requirements in the current ICAO satellite standards are insufficient to cover the quality of service (QoS) requirements of the applications supporting the future operating concept. There is therefore a need to update the AMS(R)S SARPs with more stringent performance-based requirements in line with the requirements of the future ATM concept.

In the European context, it is the European Space Agency (ESA) with the Iris Programme that is leading the satellite system design for ATM communication in collaboration with the Sesar Joint Undertaking and in particular with the project P15.2.6 .

In particular the Iris programme is complemented by the SESAR P15.2.6 project (Future mobile satellite communication) that is defined as the direct interface of SESAR Programme to the ESA Iris Programme.

Figure 2: SESAR P15.2.6 and ESA Iris Programme

III. ESA IRIS PROGRAMME

The ARTES Element 10, “Iris” Programme, is the European Space Agency’s programme in support of theimplementation of safety-of-life communications via satellite for the future European Air Traffic Management System (EATMS) .

On the basis of the communications needs and the technological solutions identified through SESAR definition phase [1][2], ESA started the Phase II.1 of the Iris programme which aim at designing, developing, verifying and validating the new satellite communication system for ATM services within the framework of the SESAR Programme.

Indeed the Iris Programme is financed by ESA MemberStates but it’s implemented in close collaboration with SESAR Joint Undertaking.

The collaboration is highlighted by the Iris Mission Requirements definition and the validation of the satellite system that are in charge to SESAR Joint Undertaking (SJU).

Furthermore SJU has been involved in the formal review process of the ESA Iris projects contributing to the System Requirements Review board with Eurocontrol, the European Aviation Safety Agency (EASA), Airbus and an independent ATM Safety Board.

The design and development of the satellite communication system for ATM implies two parallel developments:

o the development of a new satellite communication standard to be adopted at worldwide level, designed ad hoc for the future ATM services being also an open standard.

o the development of a European satellite infrastructure, which will enable the provision of the future ATM services service in the defined coverage area.

From a technical perspective, the ESA role with the Iris Programme covers the design of the new Communication Standard, the design and development of the Satellite Communication System, the procurement of the subset and the System Verification.

The Iris Phase II.1 has started in 2009, studying two alternative approaches for the system design:

o the design of a purpose-built system (called ANTARES) supported by three parallel studies preparing for future service provision (HERMES, OPERA and SIRIO studies),

o the analysis of the feasibility of adapting Inmarsat’s SwiftBroadband system for provision of safety services (THAUMAS study). This activity includes modifications of the SBB communication system, the analysis of required modifications to the satellite constellation, user terminals and the ground segment.

Based on the result of the Iris Phase II.1 studies, ESA will submit a proposal for a Satcom system solution to SJU at the end of the 2012. The ESA proposal will include different technical options (based on ANTARES and THAUMAS solutions) in terms of geographical service coverage and system capacity.


The technical options will be supported by Business Cases, Safety Cases and Security Case.

Based on all these elements, EC and SJU will reach a consensus on the final design option of the Satcom solution for SESAR.

A decision by ESA Member States is forecast in 2012 to give the go-ahead for the Phase II.2 and complete the Iris Phase II in full alignment with the SESAR Development Phase.

At the end of Phase II, all transferable ESA Iris Programme assets will be transferred to the future owner of the system.

A Phase III of the ESA Iris programme foresees for ESA the only role to provide technical support to the system owner.

.

Figure 3: ESA Iris Programme logo

IV. SESAR P15.2.6 AND THE “FUTURE MOBILE SATELLITE

COMMUNICATION” LINK

P15.2.6 is carried out by the following SESAR Members. Airbus, AENA, Alenia, Eurocontrol, Frequentis, Indra, NORACON and Thales.

Thales Alenia Space Italia (one of the 4 Thales Entities involved in the project) is in charge of the project coordination.

P15.2.6 is part of the SESAR WP 15 “the Non AvionicCNS System” addressing CNS technologies development and validation also considering their compatibility with the Military and General Aviation user needs and constitutes, as previously indicated, the interface of SESAR Programme to the ESA Iris Programme.

P15.2.6 is started in April 2010 and its completion is foreseen in 2016 (inline with the ESA Iris and P15.2.4 schedule)

P15.2.6 aims to perform the following main activities:

1. Define the Iris mission requirements identifying the satellite link operational concepts and define the SATCOM system interfaces with the overall SESAR infrastructure

2. Validate the ESA Iris development from an end-to-end point of view in collaboration with P15.2.4 for the FCI Operational Concept

3. Supporting the standardizations process related with satellite communication system and promoting this

aviation technical standard to regional and international standardizations bodies.

The definition of the new datalink requirements, the data link validation and standardization is an activity implying a coordination not only with Iris Programme and P15.2.4 but in addition with the other SESAR WPs working on tasks complementary to P15.2.6 tasks. In particular links have been identified with the P15.1.6 for the satellite spectrum assessment, WP 9 for the Aeronautical Flexible User Terminal definition, WP 3 for the Validation and Verification exercises coordination, WP 10 for the ground network, WP 14 for the SWIM application and WP 16 for the Cost Benefit Analysis.

Figure 4: The 16 WPs of the SESAR Programme

Next figure summarizes the links between the Iris Programme , P15.2.6 and the other SESAR Projects and WPs:

Figure 5: Links between the Iris Programme and the other SESAR WPs.


Figure 6: Links between P15.2.6, the Iris Programme and the other SESAR Projects

V. SESAR P15.2.6 IRIS MISSION REQUIREMENT

DEFINITION

Following the E-OCVM [12], the SESAR reference for the ATM Concept Validation, the level of maturity of a new concept as the usage of a new satellite datalink for the ATM services implies an acceptance and a commitment from all involved stakeholders. Moreover the expectations of all the involved stakeholders have been discovered and collected in the Mission requirement Document.

Once the MRD is issued, ESA will translate the Mission Requirements generated by the stakeholders into requirements applicable to the design and development of the Satellite Communication System performed in the Iris Programme. These requirements will be collected in the Iris System Requirement Document (SRD).

The satellite system design is carried out in an iterative process between Iris design studies and SESAR P15.2.6. Based on inputs from P15.2.6, Iris Programme will consolidate technical requirements in an iterative process consisting of the following steps:

1. Mission (User) Requirements capture from the SJUand the Aviation Community,

2. Translation of Mission Requirements into System Requirements by ESA,

3. Assessment of impact of System Requirements on the design of the Satellite Communication System

4. feed-back on such impacts and their high-level consequence to SJU and the Aviation Community, leading to a possible update of the Mission Requirements and a new iteration cycle.

The following stakeholders have been identified for the Satellite Communication System:

• the SESAR JU is responsible for the definition of the users requirements for the overall European ATM System, for the apportionment of technical budgets requirements among its

different communication systems (Terrestrial System, Satellite System, Airport network) and for the definition of the interfaces between these systems.

• The EC is responsible for establishing the futureregulation for the deployment of SESAR and for the provision of services within the SES legislation.

• ESA will be responsible for the management and coordination of the design, development and verification activities of the satellite communication system until the validation phase. At the end of the development phase, ESA will transfer the assets to the system owner.

• The European Aviation Safety Agency (EASA) will be responsible for future safety regulations for ATM, according to the SES legislation.

• Satellite and Aeronautical Manufacturing Industrywill develop the satellite system and the user terminals

• Satellite Operators and Satellite Service Providers (Satellite Communication Service Providers and Communication Service Providers) will be in charge to operate the satellite and provide the service to the final users of the system (ANSPs, Airlines, General Aviation).

• The Future System Owner is foreseen to be identified at the end of the next phase of Iris Programme.

• The International Civil Aviation Organisation (ICAO) will be involved to approve and endorse the SARPs and required updates to the AMS(R)S Technical Manual (a new Part)

• the Aeronautical Community (ANSPs, Airspace usersand industry associations such as IATA) as final users of the system.

Representatives of all the stakeholders have been involved in the MRD definition.

In particular SESAR P15.2.6 MRD definition task is carried out with the involvement of the Satellite and Aeronautical Manufacturing Industry (Thales, Indra,Frequentis, Airbus), a Satellite Operator and Satellite Service Provider (Telespazio is the affiliate of Alenia Member involved in the project), 2 ANSPs (AENA and Avinor - member of Noracon) and Eurocontrol.

Airspace Users contribution to the P15.2.6 MRD definition task and SESAR projects more in general is also planned to be obtained by SJU contracts with Air France, EBAA, KLM, Iberia, the Lufthansa Group, Novair, SAS Scandinavian Airlines, TAP Portugal, IATA, IAOPA and ELFAA [13].

In this way SJU has reinforced the user-driven approach to technologies and procedures development including the Airspace Users on the analysis of the outcomes of SESAR projects [13].

In particular EBAA and SAS will be directly involved in the MRD review.


The involvement of EASA is obtained by Iris Programme where EASA is involved for the Expert technical safety advice to ESA and for the chair of the ATM SATCOM (Iris) Safety Board (ASSB) [14].

Satellite Operators and Satellite Service Providers are involved in the Service Operators Studies (SIRIO, OPERA, HERMES) of the Iris Programme.

Currently the mission requirements are under definition within P15.2.6 and the Aviation Community. The mainrequirements having strong impact on the Satellite system design are reported in the following:

o the coverage area of interest for the provision of the SATCOM services

o the list of ATC and AOC services to be included and the reference performances (reference are COCR [15] and the outcomes of P15.2.4 and other SJU external studies as AOC Study and OPTIMI)

o the validation assumptions as the foreseen infrastructure and the timeframe

o security features and countermeasure to the impact of interferences on the mobile link.

Figure 7: SES Airspace and European FABs are investigated by P15.2.6 as possible coverage areas With the Iris MRD, P15.2.6 will deliver to ESA the Iris ICD that will define the interface between the future European ATM Network (EATMN) with the Iris SATCOM system.

VI. SESAR P15.2.6 ESA IRIS DEVELOPMENT VALIDATION

The reference in SESAR for the validation and verification methodology is E-OCVM [12].

Validation is intended moreover as an iterative process providing evidence that a new system or operational concept fits for purpose or, in other words, validation answers the question: “Are we building the right system?”

Verification provides proof that technology components are feasible and can be safely and economically implemented or, in other words, verification answers the question: “Are we building the system right?”

Verification shall be conducted in parallel with validation in order to discover problems early and to resolve them before costly deployment. Indeed Validation and Verification (V&V) are not considered in isolation by the E-OCVM but are combined.

P15.2.6 is a System project and for this reason validation is not part on the scope of the project that will be focused on the System verification. However P15.2.6 will support the operational validation exercises within WP3 and/or individual Operational projects to be identified in the near future.

The current WP3 objective is to “build a comprehensive and integrated V&V Infrastructure to fulfil the overall validation needs for the development of the SESAR ATM system. The resulting V&V Infrastructure should be capable of validating from a single ATM Service up to the whole SESAR ATM Target Concept ...”.[16]

WP3 will provide support to the Operational projects for the setting up of validation activities plus the specification and development of specific validation tools.

Main tools are the IBPs representing the Industry Based/Pre-Operational V&V Platforms.

Main tasks of the V&V activity performed in P15.2.6 are :

o Definition of the SATCOM system test procedures andplan

o Specification of the Test bed(s) requirements

o Definition of the test bed architecture to integrate the Iris SATCOM component inside the SESAR IBPs validation platform

o Support to the integration of the SATCOM test bed platform(s) developed in the Iris Programme in the FCI Verification Test Bed for the end to end V&V activities

o Execution of the tests and collection of the results.

The boundaries SATCOM datalink defines the boundaries of the P15.2.6 V&V activity respect the FCI V&V. The assumption is that the V&V task of the satellite communication system will be carried out between the interface between the Satellite Communication Service Provider and the Communication Service Provider on the one end and the interface between the Satellite Data Unit and the on-board router of the aircraft on the other end.

This is depicted in the following scheme (from [18]):


Figure 8: Boundaries of the Satellite CommunicationSystem V&V

A possible architecture of the FCI Test bed platform for V&V purposes is reported in the next figure (from [17]):

Verification platform and test controler

ATN/IPS ground infrastructure mockup (including A/G router,

G/G router, etc)

LDACS AGDL

emulator

ATN/IPS airborne infrastructure mockup

SATCOM AGDL

emulator

AeroMACS AGDL

emulator

ATC/AOCTraffic

Generator

Figure 9: A possible FCI Test bed platform architecture to support V&V of the SATCOM datalink from an end to end perspective.

P15.2.4 FCI test bed will be connected to “Air Ground Data Link” AGDL emulators or prototypes developed in P15.2.4 (LDACS), P15.2.6/Iris (SATCOM) and P15.2.7 (AeroMACS)

P15.2.4 FCI test bed will support V&V of the SATCOMdatalink from an end to end perspective and in particular:

• Verification exercises where the FCI architecture (and multilink management) impacts the SATCOM datalink performances ;

• Validation exercises performed with the context of WP3 or associated operational projects, where the performance of the FCI impacts the satcom datalink and where the FCI and

the SATCOM in particular provides the communicationchannel to be connected to the SESAR IBPs.

The SATCOM test bed could be used in addition (if agreed in the test bed requirement document) for:

• Demonstrations,

• Certification exercises.

VII. SUPPORTING THE STANDARDIZATIONS PROCESS

Because aviation operates on a global basis, any new ATM solution must be supported and coordinated on a worldwide basis. This requires ICAO standardisation and coordination with the international stakeholders leading to support for the new standard.

For this reason SJU under the US/EC Memorandum Of Cooperation agreed to establish a dedicated activity (“Coordination PLAN 4.4 Data-link technology“) to coordinate FAA the development of future communication technologies, including the supporting avionics architecture and in particular including developments of LDACS, Aeromacs and future SATCOM technologies.

The activities in this Coordination Plan are very much linked to providing input to aviation standardisation groups such as ICAO (ACP), EUROCAE, RTCA and (maybe) SAE.

Furthermore P15.2.6 supports the standardization activities and will drive the development of the required global ICAO standards by the involvement of its Members in a dedicated Working Group (WG). The international aspect of satellite system standardization are dealt in the EUROCONTROLNEXUS WG with contributions from all interested parties in Europe and other interested Countries such as USA and Japan:.

NEXUS WG is a subgroup of Nexsat WG and is based onvoluntary contributions. EUROCONTROL is the facilitator (“rapporteur “) of the discussions in the group [19]. NEXUS is currently working in developing a proposal for ICAO for an update of the AMS(R)S SARPs with stringent performance requirements and in the future will be involved in developing a proposal for an update of the AMS(R)S Technical Manual with the specifications of a new SATCOM technology meeting the updated requirements. This imply a strict coordination between the work carried out in the Nexus WG and the Mission Requirement definition.

VIII. CONCLUSION

The main objectives of the Project 15.2.6 “Future mobile satellite communication” is to define the requirements for the future Satellite Communication System that will be developed by ESA in the Iris Programme, to perform the technical validation of the Iris SATCOM system, support the standardization of the new satellite link with aviation standardization groups such as ICAO and EUROCAE.

A new concept as the usage of a new satellite datalink for the ATM services as part of a more general multilink


operational concept implies an acceptance and a commitment from all the users and service providers.

P15.2.6 aims moreover to identify, define and collect the expectations of all the involved aeronautical stakeholders in order to feed the ESA Iris Programme with requirements that will bring ESA to develop a system corresponding to the Airspace Users and ANSPs expectations for the future.

Operational validation of the SATCOM system is in charge to SESAR ANSPs Operational projects by the methodologies and tools made available by WP3. P15.2.6 will be focused mainly on the system verification and will support the operational validation process. Verification activities will consist on defining the SATCOM system test procedures and plan, the specification of the SATCOM Test bed architecture, the support to the integration of the SATCOM test bed in the FCI test bed and the execution and analysis of the related tests.

The standardization activities for the new SATCOM datalink are supported by P15.2.6 with the involvement of P15.2.6 Members in the Nexus Group aimed to deliver to ICAO an agreed amendment to the ICAO SATCOM SARPs (Annex 10 Vol. 3).

First issue of Iris MRD will be provided to ESA within 2011 and will be updated in a iterative way as soon as the FCI concepts will be consolidated.

REFERENCES

[1] SESAR The ATM Target Concept (D3), DLM-0612-001-02-00a (approved), September 2007.(Page 11)

[2] "Technology Assessment", SESAR Definition Phase Task 2.5.x - Milestone 3. SESAR Consortium, 2007. DLT-0612-25x-00-05. (Sec. 1.5.1).

[3] SESAR The ATM Target Concept (D3), DLM-0612-001-02-00a (approved), September 2007.(Page 52)

[4] European Air Traffic Management Master Plan Edition 1 - 30 March 2009 (Pag. 101)

[5] SESAR Deployment sequence (D4), DLM-0706-001-02-00 (approved) January 2008 (pag. 77)

[6] “Action Plan 17: Future Communications Study – Final Conclusions and Recommendations Report”, EUROCONTROL/FAA, Version 1.1, November 2007

[7] http://telecom.esa.int/iris

[8] ESA Iris Programme Status and proposed way forward Document produced by ESA, EC and SESAR JU Version 20 September 2010

[9] Inter-Regional Satcom Voice Ad Hoc Task Force (SATCOM ad hoc TF) First Meeting (Paris, France, 25 to 27 January 2011) Agenda Item 4: Review of the status of implementation and available documentation Status Of ESA Iris Programme

[10] Project P15.2.4 DEL EWA02-T1-D1 Multilink Concept: An Operational Perspective edition. 00.00.08 (14/02/2011)

[11] The EUROCONTROL Skyway magazine - Number 54 - Winter 2010

[12] E-OCVM European Operational Concept Validation Methodology V.3.0 2010

[13] SESAR Magazine Issue #6 June 2011

[14] EASA Requirements for Service Providers of aeronautical satellite mobile (en-route) Communications (CSP) Issue 1.0 10.12.2010 Iris-SB-CP-TNO-0402-ESA-C2

[15] EUROCONTROL/FAA Communications Operating Concept and Requirements for the Future Radio System Version 2.0

[16] SESAR Revision Framework for WP03 2nd June 2010 Edition 01.01.00

[17] SESAR P15.2.4 PIR 29/04/2011 Edition 00.01.00

[18] ESA Iris Programme Validation Assumptions draft 0.1

[19] Eurocontrol Nexus Terms of Reference v1.0 (www.eurocontrol.int/nexsat/public/standard_page/NEXUS.html)

[20] ICAO Document 9869, Manual on Required Communication Performance (RCP) First Edition -2008


Optimising Runway Throughput through Wake Vortex Detection, Prediction and decision

support toolsFrédéric Barbaresco, Philippe Juge,

Mathieu Klein , Yves Ricci & Jean-Yves Schneider, Surface Radar, Advanced Developments Department

Thales Air Systems, Limours, France [email protected]

Jean-François Moneuse FASNET Team Manager

Air Traffic Management Systems Thales Air Systems, Rungis, France

Jean-franç[email protected]

Abstract— Currently at many airports, runway is the limiting factor for the overall throughput. Among the most important parameters are the fixed wake turbulence separation minima expressed in time for take-off clearance and by distance for arrivals on final approach. This wake turbulence separation limits the arrival and departure flow on many airports in Europe already today. Existing departure and arrival wake turbulence separations are sometimes considered over conservative as they do not take into account meteorological conditions likely to shift, reduce or alleviate their circulations. This paper will present the main aspects of a SESAR project that defines, analyses and develops a verified wake turbulence system according to related operational concept improvements in order to, punctually or permanently, reduce landing and departure wake turbulence separations and, therefore, to increase the runway throughput in such a way that it safely absorbs arrival demand peaks and/or reduces departure delays. This global objective will be achieved by means of developing a wake vortex decision support system able to deliver in real time position and strength of the wake vortices and to predict their behavior and potential impact on safety and capacity, taking in account actual weather information as well as the airport specific climatological conditions, aircraft characteristics (generated wake vortex and wake vortex sensitivity) and airport runways layout. These functionalities will be progressively included in the wake vortex decision support system to be validated and deployed on airports in order to optimize the runway throughput and reduce delays.

Keywords- airport, wake-vortex, safety, radar, lidar

I. INTRODUCTION

Aircraft creates wake vortices in different flying phases. To avoid jeopardizing flight safety by wake vortices encounters, time/distance separations have been conservatively increased, thus restricting runway capacity. The concern is higher during taking off and landing phases, as aircraft are less easy to maneuver. These vortices usually dissipate quickly (decay due to air turbulence or transport by cross-wind), but most airports operate for the safest scenario, which means the interval between aircraft taking off or landing often amounts to several minutes. However, with the aid of accurate wind data and precise measurements of Wake Vortex, more efficient intervals can be set, particularly when weather conditions are stable.

Depending on traffic volume, these adjustments can generate capacity gains, which have major commercial benefits.

Wake vortices are a natural by-product of lift generated by aircraft and can be considered as two horizontal tornados trailing behind the aircraft. A trailing aircraft exposed to the wake vortex turbulence of a lead aircraft can experience an induced roll moment (bank angle) that is not easily corrected by the pilot or the autopilot. However these distances can be safely reduced with the aid of smart planning techniques of future Wake Vortex Decision Support Systems based on Wake Vortex detection/monitoring and Wake Vortex Prediction (mainly transport estimation by cross-wind), significantly increasing airport capacity. This limiting factor will be significantly accentuated soon with the arrival of new heavy aircrafts: Airbus A380, stretched version of Boeing B747-8.

Radar and Lidar Sensors are low cost technologies with highly performing complementary wake-vortex detection capability in all weather conditions compared to others sensors that suffer of limited one. Radar and Lidar are promising sensors for turbulences remote sensing on airport, for all kinds of aviation weather hazards (wake vortex, wind-shear, micro-bursts, atmospheric turbulences) with ability to work operationally in a collaborative way, in different severe weather conditions like fog, rain, wind, and dry air.

II. WAKE VORTEX HAZARDS

The Wake Vortices shed by an aircraft are a natural consequence of its lift. The wake flow behind an aircraft can be described by near field and far field characteristics. In the near field small vortices emerge from that vortex sheet at the wing tips and at the edges of the landing flaps.

After roll-up the wake generally consists of two coherent counter-rotating swirling flows, like horizontal tornadoes, of about equal strength: the aircraft wake vortices.

Empirical laws model tangential speed in roll-up. Classically, velocity profile (tangential speed at radius r) is defined by :

Brf

er

rv 12

)( 0 (1)


where 0 is called circulation. This Wake Vortex Circulation Strength (root circulation in m2/s) is proportional to Aircraft mass M and gravity g, inversely proportional to air density ,Wingspan B and Aircraft speed V [1] with 4/s :

.V.s.BM.g

0 (2)

Additional factors that induced specific dynamic of wake vortices: Wind Shear Effect (stratification of wind), Ground Effect (rebound), Transport by Cross-wind & Decay by atmospheric turbulence and Crow instability

Figure 1. Wake-Vortex Dynamic & behavior

III. PROJECT PHASES OF WAKE VORTEX DECISION SUPPORT SYSTEM DEVELOPMENT

Wake Vortex Decision Support System Architecture will be defined and validated during the following development phases of P12.2.2 SESAR project:

Phase 0

The preliminary system architecture will include wake vortex sensors and weather sensors. During this task, a theoretical study and a sensors benchmark campaign will be performed in Paris CDG airport (XP0 campaign) in order to select the needed sensors set. The recommendations on sensor technology selection and deployment delivered by this task will be used to refine the system architecture in the following phases.

Phase 1 - Time-Based Separation (TBS)

The aim is to verify the position, strength and behavior of the wake vortices depending on headwind strength in arrivals in order to evolve from distance based separation to time based separation. As well, a first release of the Wake Vortex Decision Support System prototype will be developed, which will demonstrate this capability. This demonstration will include an in-situ verification campaign (XP1 in CDG).

Phase 2 - Weather Dependent Separation (WDS)

The system will be updated with all the components linked to weather nowcast and forecast, including real-time prediction of micro-scale terrain-induced turbulence close to the airport. The goal is to assess in real-time the position and strength of the wake vortices and to predict their behavior for both departures and arrivals, in order to demonstrate the possibility to evolve from a time based separation to a weather dependent separation taking advantage of any favorable meteorological conditions (e.g. crosswind). This demonstration will include an in-situ verification campaign (XP2 in CDG). All building blocks regarding weather monitoring will be developed/customized.

Phase 3 - Pair Wise Separation (PWS)

The system will be refined to reach two main goals:

Perform a first demonstration of the pair wise separation concept. With a partial aircraft wake vortex characteristics database provided by P6.8.1, it will be demonstrated that the Wake Vortex Decision Support System could determine a dynamic pair wise separation, taking in account the real-time weather conditions as well as the aircraft sensitivity to wake vortex.

Demonstrate the system adaptability to other runway layouts.

These demonstrations will be performed in platform tests and verified in an in-situ campaign (XP3 in Frankfurt). Building blocks related to pair wise separation (aircraft characteristics database, algorithms…) will be developed or customized.

IV. PRELIMINARY SYSTEM ARCHITECTURE OF RUNWAYWAKE VORTEX DETECTION, PREDICTION AND DECISION

SUPPORT TOOLS

The system architecture development is based on SESAR requirements in term of safety & operational use ANSPs & EUROCONTROL Advices & requirements will be also taken into account.

Since no operational Wake Vortex Decision Support System (WVDSS) are currently available, this first framework architecture is based on existing building blocks coming from partners.

The Wake Vortex Decision Support System (WVDSS) receives as main external inputs:

The information flow (from the ATC & Airport centers) describing the current traffic and aircraft data. This function provides the air traffic flow situation to the WVDSS.

The standard information related to Weather situation (Meteo Center) as provided by National Weather Forecast Services. The Meteorological Center provides data from the operational weather forecast model “LM” of national Weather Service (e.g.


METEO FRANCE, DWD…) covering most of Europe.

The system is in charge of elaborating Decision Aids to support:

the Supervisor,

the Approach Controllers,

the Airport Tower Controllers.

HMI towards Supervisor and controllers are considered as outside WVDSS Architecture.

Figure 2. System Overview

Components of Runway Wake Vortex Detection, Prediction and Decision Support Tools are the followings :

A. Local Meteorological Sensors function A combination of sensors, which are typically weather

dependent, will be used for wind & air turbulence monitoring.

The local meteorological measurements are used for weather nowcast and forecast, through following parameters:

Mean wind: three wind components and wind variability,

Turbulence: measured as the Turbulent Kinetic Energy (TKE) or Eddy Dissipation Rate (EDR) level of the atmosphere,

Virtual potential temperature: temperature stratification

B. Wake Vortex Sensors function The wake vortex measurements will be performed with two

complementary sensors, one X band radar and one 1.5 micron Lidar. The rational is that Lidar sensor performances are limited in adverse condition as in rainy or foggy weathers.

The ability of Radar to detect & monitor Wake Vortices in rainy weather will complement Lidar in adverse weather situations.

Radar and Lidar are good complementary sensors, which can be used for turbulence remote sensing as well. They are able to work in a collaborative way, in different weather

conditions like fog, rain, strong wind, turbulent atmosphere and dry air.

C. Local Weather Nowcast/Forecast function Local Weather Nowcast & Forecast function will be able to

predict atmospheric state variables within a coverage area of e.g. 100x100 km² centered on the airport with an increasing vertical spacing from e.g. 25 to 50 m throughout the boundary layer. Output variables are vertical profiles of horizontal and vertical wind, virtual potential temperature, turbulent kinetic energy (TKE) and eddy dissipation rate (EDR).

D. Wake Vortex Advisory System function The Wake Vortex Advisory System (WVAS) will be

composed of:

an input/output (I/O) module,

a separation mode planner module,

a wake vortex predictor module,

a monitoring and alerting module

The Wake Vortex Advisory System will be able to:

Propose the separation mode to the supervisor e.g. ICAO or reduced separation and time applicability of separation mode

Process wind data including turbulence information and system track to provide spacing (chevron position for display purpose)

Monitor Wake Vortices (Wake Vortex Predictor output) against system tracks and provide Encounter Advisories to controllers HMI for display purpose in case of actual or predicted danger,

Manage the wake vortex data (4D data) from Wake Vortex Sensors function. In case of discrepancies between wake-vortex sensors and predictor, an alert is generated.

The Decision Support System functional architecture is described in the following figure:

Meteo Centre

ATC & AirportSystems

Wake Vortex Sensors

Wake Vortex Decision Support System

Local Meteo Sensors

Local Weather Nowcast & Forecast

External Weather Observations

Aircraft Characteristics + 4D trajectory

HMILocal

Weather Data Cube

(Grib)

INT-ITWS-1

INT-ITWS-2

INT-LWF-1

INT-LWF-2

INT-LWFN-1

INT-ITWS-3

INT-WVAS-1

INT-WVDET-2

INT-ATCS-1

INT-WVAS-2

Supervisor

Tower

ApproachWake Vortex Advisory

System

INT-ATCS-2

INT-WVAS-4

INT-EXT-MET

INT-WVAS-3INT-WVDET-1

Figure 3. Decision Support System functional architecture


V. SENSORS SIMULATORS

To study best sensor parameters/modes tuning and best sensors deployment on airport, simulators are mandatory. A custumized 1.5 m Lidar Wake-Vortex Simulators will be developed with UCL (Belgium). Radar Wake-Vortex Simulator activity is relatively new and specific tasks are actively engaged on their development: in collaboration with UCL (Belgium).

Figure 4. Wake-Vortex Lidar Sensor Simulator (on the left) Wake-Vortex Radar Sensor Simulator (on the right)

VI. WEATHER & ATMOSPHERIC TURBULENCES MODELS

Meteo-France will develop a new advanced Weather Forecast Model (resolution: 500 m) for airport applications : Meteorological High-Resolution Prediction System (MHRPS) MHRPS development will be based on the French non-hydrostatic AROME model. The MHRPS will be implemented on the Meteo-France super-computer and will assimilate not only dedicated airport sensors data but also all the routine data coming from the European Meteorological Infrastructure MHRPS Requirements are the following:

Required parameters: Horizontal and vertical wind (U, V, W), Temperature (T), Humidity (Hu), Eddy Dissipation Rate (EDR), Surface Pressure (PS); Required horizontal resolution: 500 m; Required coverage area: 100x100 km² centered on the airport; Required vertical resolution: 10 m up to 100 m, 100 m up to 1000 m, 1000 above; Required forecast horizon: 3 h; Required frequency of forecast outputs: 5’;

Figure 5. Sensors Data ingestion in Weather Forecast Models

NATMIG will develop Turbulence Forecast Model (grid resolution: 100 m). A Reynolds averaged Navier-Stokes model (SIMRA) has been developed by NATMIG member SINTEF in order to predict local wind and turbulence around airports.Forecast EDR/TKE model will be adapted for airport infrastructure (buildings,…)

Figure 6. Turbulent Kinetic Energy forcasted by NATMIG model

The MHPRS software of METEO FRANCE and the «Turbulences Calculation» of NATMIG will update the «Local Weather Data Cube». The data stored in « Local Weather Data Cube » are computed by MHPRS within a volume centered on airport containing following areas of interest for all trials XP0, XP1 and XP2:

Airspace allowed for landing (green color), Airspace allowed for taking off (white color), Airspace where dense traffic (arrival) is expected (blue color)

Within the volumes, the data are provided by the MHPRS for the grid points whose characteristics are:

Latitude: 48.6N to 49.4N with a quantum of 0.005° (160pts with an horizontal resolution of 550m) Longitude: 2.08N to 2.98N with a quantum of 0.005° (180pts with an horizontal resolution of 360m)

Figure 7. Area of Interests & Volumes of Weather Data Cubes


VII. DERISKING 2008 TRIALS AND SESAR XP0 CAMPAIGN AT PARIS CDG AIRPORT

In derisking phase in 2008 [3-6], THALES BOR-A radar has been deployed at Paris CDG Airport, and co-localized with a Eurocontrol 2 m Lidar. In a first step, antenna was used in a staring mode for vertical exploration by exploitation of 4° beamwidth. In the following figure, wake vortex detection are illustrated by Doppler entropy in time/range coordinates axes in rainy weather. After each departures on the first nearer runways, wake vortex are monitored.

Figure 8. wake vortex roll-ups tracking from scan to scan in rainy weather

In vertical scanning mode, individual roll-up of each wake vortex were tracked in range and elevation axes. In previous figure, above the first nearer runway, wake vortex generated by aircraft during departure can be observed. These detections of wake vortex are coherent with classical behavior close to the ground. Each roll-up from scan to scan (with one scan every 5 seconds) can be tracked as proved by the trials. Close to the ground, trajectory of each roll-up can finely and accurately been followed and their strength been estimated by circulation computation.

More recently, from mid-May to end of June 2011, first XP0 Sensors Campaign of SESAR P12.2.2 have been done at Paris CDG Airport with the following sensors :

Figure 9. SESAR P12.2.2 XP0 Sensors Campaign at Paris CDG Airport

Wake Vortex sensors : X band radar BOR-A (THALES), Windcube 200S scanner Lidar (LEOSPHERE) [8-9] Weather sensors : Windcube 70 wind profiler Lidar (LEOSPHERE), C band weather radar (METEO FRANCE), SODAR (METEO FRANCE), UHF Wind Profiler radar-PCL1300 (METEO FRANCE), UHF Wind Profiler radar-PCL1300 (DEGREA)

Figure 10. THALES Wake-Vortex X-band Radar Sensor Deployment

Figure 11. DEGREANE UHF Radar Wind Profiler Deployment

Figure 12. Meteo-France UHF Radar Wind Profiler Deployment

Figure 13. LEOSPHERE Lidar Wind Profiler Deployment


Figure 14. LEOSPHERE Wake-Vortex Lidar Deployment

Figure 15. LEOSPHERE W200 Lidar : Wind & Wake-Vortex Modes

In the following figure, Recording coordination during Paris CDG XP0 trials to prepare XP1 are illustrated.

Meteo Centre

ATC & Airport Systems

Meteo Nowcast

(Wind Profile)

Wake Vortex Location/Strength & short prediction

WV RadarData Recorder

External Weather Observations

Aircraft Characteristics + 4D trajectory

Anemometers

LIDAR Wind Profiler

UHF Wind Profiler

LIDAR scanner

SODAR/RASS

Weather Radar

Local Meteo Sensors

Lidar 1.5 um

Mechanical scan X Band Radar

Air TrafficData Recorder

Recorder

Recorder

Recorder

Recorder

Recorder

Meteo Data Recorder

Recorder

WV LidarData Recorder

Wake Vortex Sensors

Wake Vortex Decision Support System

Figure 16. Recordings coordination for XP0

ACKNOWLEDGMENT

P12.2.2. Project is funded by European SESAR Program :

http://www.sesarju.eu/

R&D Research Needs [7] are studied in WakeNet3-Europe Coordination Action project, funded by FP7:

http://www.wakenet.eu/index.php?id=21



REFERENCES

[1] F. Holzälpfel & al., “Analysis of wake vortex decay mechanisms in the atmosphere”, Aerospace Science & Technology, n°7, pp.263-275, 2003

[2] K. Shariff,“Analysis of the Radar Reflectivity of Aircraft Vortex Wakes”,J. Fluid Mech.,vol.463, pp.121-161, 2002

[3] F. Barbaresco & U. Meier, “Wake Vortex X-band Radar Monitoring : Paris-CDG airport 2008 Campaign Results & Prospectives”, International Radar Conference, Radar’09, Bordeaux, October 2009

[4] F. Barbaresco, “Interactions between Symmetric Cone and Information Geometries”, ETVC’08 Conf., Ecole Polytechnique, Nov. 2008, published by Springer, in LNCS, vol.5416, February 2009

[5] F.Barbaresco, “Radar Monitoring of Wake Vortex : Electromagnetic reflection of Wake Turbulence in clear air”, Comptes-rendus Physique Académie des Sciences, Elsevier, 2010, http://www.wakenet.eu/fileadmin/user_upload/News%26Publications/CRPhys_article.pdf (preprint)

[6] F.Barbaresco, “Airport Radar Monitoring of Wake Vortex in all Weather Conditions”, EURAD’11, EuMW, Paris, September 2010, http://www.wakenet.eu/fileadmin/user_upload/News%26Publications/EURAD-Wake-Vortex-Barbaresco.pdf (preprint)

[7] M. Steen, S. Schönhals, J. Polvinen, P. Drake, J.P. Cariou, A. Dolfi-Bouteyre, F. Barbaresco, “Airport Radar Monitoring of Wake Vortex in all Weather Conditions”, 9th Innovative Research Workshop & Exhibition, EUROCONTROL E.C, France, December 7 - 9, 2010, http://www.wakenet.eu/fileadmin/user_upload/News%26Publications/INO-WS2010_148_48085-1.pdf (preprint)

[8] A. Dolfi-Bouteyre, B. Augere, M. Valla, D. Goular, D. Fleury, G. Canat, C. Planchat, T. Gaudo, C. Besson, A. Gilliot, J.-P. Cariou, O. Petilon, J. Lawson-Daku, S. Brousmiche, S. Lugan, L. Bricteux, B. Macq, “Aircraft wake vortex study and characterization with 1.5 m fiber Doppler lidar”, Journal of Aerospace Lab, December 2009

[9] S. Schönhals, M. Steen, P. Hecker, “Surveillance Systems On-Board Aircraft: Predicting, Detecting and Tracking Wake Vortices“, Proc. of 8th Innovative Research Workshop, pp.65, Dec. 2009, Eurocontrol

[10] S. Schönhals, M. Steen, P. Hecker, “European Air Traffic Management Master Plan“, Edition 1, 30 March 2009, SESAR Consortium

[11] Hahn et al: "Wake Encounter Flight Control Assistance Based on Forward-Looking Measurement Processing", AIAA Atmospheric and Space Environments Conf., Toronto, Canada August 2010

[12] Schwarz et al: "Wake Encoutner Severity Assessment Based on Validated Aerodynamic Interaction Models ", AIAA Atmospheric and Space Environments Conf. AIAA 2010, Toronto, Canada August 2010

[13] Kocks et al: "An integrated Wake Vortex Visualization Concept for existing cockpit display systems ", Proc. EIWAC2010 Conf., 2010

[14] Schoenhals et al: "Enhancing Wake Vortex Surveillance Capability Using Innovative Fusion Approaches ", Proc. EIWAC2010 conf., 2010

[15] ATC-WAKE – ATC Wake System Design and Evaluation; ATC Wake D2_12; 31/12/2005, http://www.nlr.nl/eCache/DEF/502.html

[16] ATC-WAKE – ATC Wake System Requirements; 31/12/2005 [17] CREDOS – Operational and System Requirements; v 1.0; 2008/03/31 [18] SESAR 12.02.02 ID D01 Report vers.00.01.00, “Preliminary System

Requirements of Runway Wake Vortex Detection, Prediction and decision support tools”, 15th July 2010

[19] SESAR 12.02.02 ID D02 Report vers.00.01.00, “Preliminary System Architecture of Runway Wake Vortex Detection, Prediction and Decision Support Tools”, 27th October 2010


ADS B and WAM deployment in EuropeSession 2.1 page 35

U.S. Activities in ADS B Systems ImplementationSession 2.2 page 41

Detect and avoid for Unmanned Aircraft Systems inthe total system approach

Session 2.3 page 47

North Sea Helicopter ADS B/MLat Pilot Project Findings

Session 2.4 page 53


ADS-B and WAM deployment in Europe

C. Rekkas Directorate Network Management - EUROCONTROL

Rue de la Fusée 96, B-1130 Brussels, Belgium [email protected]

Abstract— The paper provides an update on the status and plans of the deployment of Automatic Dependent Surveillance-Broadcast (ADS-B) and Wide Area Multilateration (WAM) in Europe. It presents the multiple deployment activities throughout the continent as well as the achievements in areas such as standardisation, certification, safety, trials, monitoring etc. The deployment of ADS-B and WAM in Europe is progressing fast. Initial operational implementations of WAM already exist, whilst the first ADS-B operations are expected at local sites of Non Radar Airspace in 2011. Furthermore, a large number of European ANSPs will deploy ADS-B and WAM systems from 2012 onwards, ensuring the availability of ground infrastructure. Several hundreds of aircraft are already certified for ADS-B in Non-Radar Airspace. In parallel, the expected approval of the Single European Sky (SES) Implementing Rule on Surveillance Performance and Interoperability (SPI IR) by the European Commission in the course of this year will enable the widespread aircraft equipage for other ADS-B applications and will thus accelerate the implementation of ADS-B ground Surveillance applications in Europe from 2015-2017 onwards. In addition, a major objective will be achieved in 2011 with the initial operations of Airborne Traffic Situational Awareness (ATSAW) applications over N. Atlantic. The significant progress achieved will enable important benefits for the ATM Network and establish the Surveillance foundation for the SESAR Programme, including the deployment of future more demanding ADS-B applications.

Keywords- 1090 MHz receiver;

I. INTRODUCTION

ADS-B is a Surveillance technique that relies on aircraft broadcasting their identity, position and other aircraft information. This signal can be captured for Surveillance purposes on the ground (ADS-B out) or on board other aircraft/vehicles (ADS-B in). The latter will enable airborne traffic situational awareness (ATSAW), spacing, separation and self-separation applications.

Wide-area multilateration (WAM) is a Surveillance technique that exploits the 1090 MHz transmissions broadcast from aircraft. From these signals it can create a track containing parameters such as aircraft identification, position, height, etc. Active interrogation is also possible in order to trigger transmission.

Although the manner in which WAM constructs Surveillance data differs significantly from ADS-B, the synergies between these two Surveillance techniques in addition to their high performance and lower cost are

expected to bring significant operational benefits. Consequently, hybrid WAM/ADS-B systems are widely offered by industry and deployed by ANSPs in Europe and worldwide, thus exploiting these synergies.

However, the optimal mix of the various Surveillance techniques (SSR Mode S, ADS-B, WAM) depends on the local environment, operational needs and business case, from an overall ATM Network viewpoint.

The EUROCONTROL CASCADE Programme (part of the Directorate Network Management), co-ordinates the deployment of initial ADS-B applications and WAM in Europe. The Programme covers both ground Surveillance (i.e. “ADS-B out” and WAM) as well as airborne Surveillance applications (i.e. “ADS-B in”/ATSAW). It works actively to ensure global interoperability.

ADS-B and/or WAM are currently being implemented in Europe and other areas worldwide (Asia, Australia, Canada, USA).

II. STRATEGIC AND ATM NETWORKMANAGEMENT CONTEXT

ADS-B and WAM are key enablers of the future European ATM Network, contributing to the achievement of the Single European Sky (SES) performance objectives, including safety, capacity, efficiency and environmental sustainability.

The vision for ground Surveillance, as outlined in [2], foresees in en-route and terminal areas the combination of ADS-B with independent Surveillance, the latter provided by MSSR or Mode S or Wide Area Multilateration.

Furthermore, airborne ADS-B systems will be available as enablers of the new separation modes. These airborne applications will require changes in the avionics (“ADS-B out” and “ADS-B in”) to process and display the air situation picture to the pilot.

For airports, a locally optimised mix of the available technologies, i.e. airport Multilateration, Surface Movement Radars and ADS-B, will enable A-SMGCS systems and integrated airport operations. This includes the availability of Surveillance information on a moving map, using an HMI in the cockpit and in surface vehicles.


The introduction of ADS-B in the Surveillance infrastructure provides important features which can be exploited by the ATM Network:

Full “Network-wide” Surveillance coverage

Surveillance “everywhere”, i.e. no gaps from gate-to-gate

Air-to-air Surveillance possible, i.e. traffic situational awareness picture available on board

The aircraft is integral part of the Network

Surveillance data provided directly from on-board systems

High performance

Improved safety

Increased capacity

Cost-efficiency

Reduced cost of the Surveillance infrastructure (ADS-B and WAM are cheaper than radar)

More efficient flight profiles (in areas where previously surveillance was not cost-effective)

Fuel savings etc.

Environmental sustainability (CO2 reduction)

Reduced RF pollution (leading to an increased viability of the 1090 MHz datalink)

Global Interoperability

Foundation for future SESAR ATC applications (spacing, separation, self-separation)

The European Commission Single European Sky Surveillance Performance and Interoperability Implementing Rule (EC SES SPI IR) is in the last phase of consultation and expected to be approved and published in the Official Journal of the European Union in the course of 2011 [3]. According to the SPI IR, all aircraft operating IFR/GAT in Europe will have to be compliant with Mode S Elementary Surveillance, whilst aircraft with maximum Take-Off Mass greater than 5700kg or maximum cruising True Air Speed greater than 250kts will have to be compliant with Mode S Enhanced Surveillance and “ADS-B out”. The mandate dates currently proposed are January 2015 for forward fit and December 2017 for retrofit, with further provisions for State aircraft.

This Rule will accelerate both the aircraft ADS-B equipage and the ADS-B ground system deployment.

The new Surveillance techniques ADS-B and WAM, supported by the Rule, will enable the deployment of a rationalised (i.e. cost-efficient and spectrum efficient), high performance and interoperable Surveillance infrastructure.

III. STANDARDISATION AND CERTIFICATION The ADS-B standardisation work was driven by the

Requirements Focus Group (RFG), with principal

membership from EUROCONTROL, FAA, EUROCAE, RTCA and participation from Australia, Canada and Japan.

The ADS-B standardisation work is now completed for all “ADS-B out” and ATSAW applications. It has delivered the Safety, Performance and Interoperability Requirements for:

ADS-B in Non Radar Airspace (ADS-B NRA) [4]

ADS-B in Radar Airspace (ADS-B RAD) [5]

ADS-B for Airport Surface Surveillance (ADS-B APT) [6]

ATSAW In-Trail Procedure in oceanic airspace (ATSAW ITP) [7]

ATSAW Visual Separation in Approach (ATSAW VSA) [8]

ATSAW during Flight Operations (ATSAW AIRB) [9]

ATSAW on the Airport Surface (ATSAW SURF) [10]

In addition, the standardisation of the first spacing application has also been completed with the delivery of the Safety, Performance and Interoperability Requirements for:

Flight Deck Interval Management (ASPA-FIM) [11]

The internationally harmonized standardisation work, including the co-ordination through ICAO, enables global interoperability and ensures that equipped aircraft can use their installations worldwide.

Regarding airworthiness approval, the first milestone for ADS-B-NRA was achieved through the EASA AMC 20-24 (“Acceptable Means of Compliance”) material, published in 2008 [12].

On a worldwide scale, the EASA ADS-B-NRA Airworthiness approval was applied to Australia and Canada (Hudson-Bay). Other implementations are expected to follow (e.g. Iceland, Portugal). In addition, ADS-B-NRA certification is expected to also support early “ADS-B in” implementations (e.g. ATSAW AIRB and ITP).

On the ground side, the Technical Specifications for the ADS-B Ground Station in support of ADS-B NRA are published by EUROCAE (ED-129) [13].

The next milestone of the EASA airworthiness approval is the certification of “ADS-B out” avionics, to be compliant with the European Commission SPI IR mandate. The relevant Certification Specification material is expected to be published by mid-2012. It will cover ADS-B as a complement to radar, even in high-density airspace (ADS-B RAD), the airport applications (ADS-B APT and ATSAW SURF) and the “ADS-B out” requirements of ATSAW applications .

On the airborne side, the new standard for ADS-B 1090 ES (ED-102A/DO-260B) has been published by EUROCAE and RTCA [14]. This will supersede the existing standards (ED-102 / DO-260 and DO-260A) and will become Means of Compliance for the SPI IR.


The follow-up work for the Technical Specifications of the ADS-B Ground Station in support of “ADS-B out” applications (such as ADS-B RAD), which will ensure compliance with the Implementing Rule (ED-129A) is expected to be launched in 2011 and will get inputs from (amongst others) relevant SESAR projects.

Regarding “ADS-B in”, joint work by EUROCAE and RTCA is ongoing on the development of Aircraft Surveillance Application (ASA) MOPS. The target date for approval of this document (ED194/DO317A) is October 2011. The MOPS will form the key reference for the related EASA “ADS-B in” certification regulation.

Standardisation work focusing on the needs of General Aviation in terms of “ADS-B out” and “ADS-B in” has also started.

Regarding WAM, the Technical Specification for Wide Area Multilateration system (ED-142) has also been published by EUROCAE [15].

IV. VALIDATION

The validation work of CASCADE addresses the ADS-B systems and applications, primarily through trials transitioning to deployment. It includes the testing and verification at infrastructure level as well as validation at operational application level.

The CASCADE validation work seek also to make best use of the EUROCONTROL investments e.g. the ADS-B Validation Testbed (AVT), which is the reference platform for the ADS-B validation work in Europe.

CRISTAL Projects

CRISTAL projects have one clear objective: to perform trials in partnership with stakeholders in defined local airspace of Europe (“pocket areas”) where the Surveillance service can be improved and implementation is envisaged. The partnerships with the stakeholders have accelerated the progress from validation to implementation and generated wide stakeholder involvement.

The CRISTAL “ADS-B out” Ground implementation project, which is currently ongoing by CASCADE in partnership with 7 ANSPs, is a cornerstone of the validation work and its clear emphasis is on ground deployment of operational compliant ADS-B and/or WAM systems. The project is a key complement to the airborne deployments ongoing through the pioneer airline schemes and, more importantly, expected through the SPI Implementing Rule. The project covers both Non Radar Airspace and Radar Airspace. The Air Navigation Service Providers (ANSPs) which participate in this project are:

AVINOR, Norway

BULATSA, Bulgaria

DCA, Cyprus

DFS, Germany

HCAA, Greece

ISAVIA, Iceland and NAVIAIR, Denmark

The projects kicked off in the end of 2009 and will last until 2012-2013 depending on the case.

Moreover, the CRISTAL RAD High Density project in partnership with UK NATS is also ongoing. This project addresses the validation of ADS-B/WAM in the airspace of the London Terminal Control Area, which is one of the most complex and highest density airspaces in the world. It has also studied the future availability of aircraft-derived data (ADS-B ADD application) on the basis of the new standardised technology (ED102A/D0260B). Regarding the latter point it provided recommendations for the availability of Enhanced Surveillance data through active interrogation by an ADS-B/WAM system.

Furthermore, the CRISTAL Dual Link Interoperability project, in partnership with the Swedish ANSP LFV, validates ADS-B in a dual link configuration (1090 Ext. Squitter/VDL Mode 4), in both non-radar airspace (Kiruna) and radar airspace (Stockholm Arlanda).

Pioneer Airline Projects

In parallel to the ground implementation related projects presented above, two pioneer airline projects were launched: the first pioneer project aiming at airworthiness approval for ADS-B in Non Radar airspace was successfully completed (with 18 airlines, more than 500 aircraft and 14 different aircraft types), whereas a second pioneer airline project (on ATSAW) was recently launched.

Through the ATSAW Pioneer project, the EUROCONTROL CASCADE Programme is partnering with airlines, ANSPs and industry in order to catalyse the operational use of ADS-B to provide an airborne traffic situation picture to the flight crew.

The objective of the ATSAW Pioneer airline project is to assist airlines in equipping aircraft with certified ATSAW equipment and participating in trial operations, later transitioning to regular operations. The specific ATSAW applications targeted by the project are ATSAW AIRB, ATSAW ITP, and, at a later stage, ATSAW VSA and ATSAW SURF. The project kicked off in the end of 2009.

Five airlines have started equipping aircraft (25 in total) with certified ATSAW equipment and will participate in trial operations from 2011 onwards over N. Atlantic, i.e.

British Airways

Delta

Swiss International Airlines

US Airways

Virgin Atlantic

Two ANSPs are also involved in order to develop the ground enhancements which are required for the initial deployment of ATSAW ITP:


ISAVIA of Iceland (for the Reyjkavik FIR)

UK NATS (for the Shanwick FIR)

The ATSAW Pioneer project will mark the first operational use of ATSAW applications and pave the way for the deployment of other “ADS-B in” applications.

V. MONITORING The CASCADE ADS-B Monitoring Project objective is

to assess the performance of ADS-B. The performance of the pioneer and other aircraft is monitored using ADS-B data gathered through a network of ADS-B ground stations deployed in Europe, as well as radar and multilateration data. In total more than 1000 aircraft are currently monitored and billions of reports have been analysed.

The ADS-B Monitoring Project includes the following activities:

Monitoring the ADS-B 1090 Extended Squitter equipage

Monitoring the ADS-B data quality, for aircraft, while airborne, using ADS-B and radar data. The analysis is based on the comparison with respect to the corresponding requirements specified in the standards for ADS-B (such as [4, 5, 12]). The aircraft are monitored by the following ADS-B 1090 Extended Squitter ground stations:

Athens (HCAA), Greece

EUROCONTROL Experimental Centre (EEC), Bretigny, France

Langen (DFS), Germany

Schiphol (LVNL), Netherlands

Toulouse (DSNA), France

Warlingham (NATS), UK

Monitoring the ADS-B 1090 ES data quality, for aircraft on the airport surface, using ADS-B 1090 ES and multilateration data. The ADS-B monitoring project receives recordings from the MLAT system of:

Charles de Gaulle airport (DSNA), France

Schiphol airport (LVNL), Netherlands

Monitoring of GNSS related data continuity expressed as Mean Time Between Outage (MTBO). The results are compared with the requirement of ADS-B RAD standard [5].

A database with the results of the accuracy analysis for all aircraft participating in the project is maintained.

The results of the analysis from the pioneer aircraft show compliance with the requirements for ADS-B NRA [12].

For a part of the pioneer aircraft, the results of, in particular, the accuracy analysis show compliance also with the relevant more demanding requirements of ADS-B RAD [5].

The few “ADS-B out” anomalies which have been identified from the analysis so far have been investigated and resolved in co-operation with the airlines, airframers and avionics industries.

Regarding the ADS-B performance of pioneer aircraft on the airport surface, the results from the analysis show that:

The ADS-B position of pioneer aircraft on the airport surface is very accurate for the analysed configurations (Fig 1).

The corresponding quality indicator is generally underestimating the actual position quality.

Figure 1. ADS-B errors on the airport surface for various aircraft configuration

Furthermore, a CASCADE study on the capacity of the 1090 MHz datalink in a high-density airspace (follow-up of previous work in this area) was successfully completed, in addition to actual RF measurements using airborne test equipment. These will be used in standardisation work for the ADS-B Ground station as well as for spectrum management related activities.

VI. SAFETY CASCADE safety work encompasses a wide range of

activities, from international standardisation to support to local implementation.

In international ADS-B standardisation (joint EUROCAE/RTCA standards), CASCADE ensures that European Commission and EUROCONTROL rules and safety methods (Regulation EC No. 2096/2005, ESARR 4, SAME etc.) are duly taken into account.

Moreover, CASCADE leads the development of Preliminary Safety Cases (PSCs) aimed at demonstrating that each ADS-B application has the potential to be acceptably safe in a typical environment.

PSCs can be largely re-used for the development of Local Safety Cases (LSCs), and for that purpose include significant guidance material to facilitate the work of the ANSPs.

Across Position Error 95% for AMC 20-24 certified aircraftMonitoring period January 2010 March 2011

0

2

4

6

8

10

12

14

AcT3 AcT8 AcT7 AcT2 AcT8 AcT8 AcT3 AcT16 AcT23 AcT2 AcT3 AcT4 AcT27 AcT7 AcT28 AcT2 AcT3 AcT4 AcT16 AcT8 AcT13 AcT2

Op2 Op4 Op10 Op12 Op1 Op19 Op16 Op6 Op15

Y

ICAO aircraft type

AC

PE 9

5% (m

eter

s)

XpT1 - GT2

XpT2 - GT2XpT2 - GT3


XpT3 - GT2



The early exposure of the PSC, to the review process conducted by representatives of national supervisory authorities, is also expected to facilitate and accelerate the approval process at national regulatory level.

The Preliminary Safety Cases for ADS-B NRA and ADS-B RAD have been completed [16, 17]. The PSCs for ADS-B APT and VSA are close to completion, expected to be followed by the ones for the other ADS-B applications. In parallel, work has started on the PSCs for WAM NRA and WAM RAD.

VII. DEPLOYMENT The ADS-B and WAM deployment in Europe is now

ongoing, following three paths:

Voluntary implementation of ADS-B sole means or with WAM in local Non Radar airspace of Europe (“pocket areas”), using currently existing (certified) equipment, from 2011 onwards

Deployment of WAM and ADS-B systems in Radar Airspace, in which WAM is used first, followed by the use of “ADS-B out”. The latter requires enhanced avionics and is, therefore, driven by the Implementing Rule (SPI IR).

In parallel, voluntary implementation of Airborne Traffic Situational Awareness (ATSAW) applications starts in oceanic areas in the course of 2011.

WAM is already implemented in Armenia, Austria, Czech Republic, Spain and UK (N. Sea).

In addition, WAM and ADS-B deployment is currently ongoing in Germany (WAM by 2012 in Frankfurt, expected to be followed by Munich and Berlin, which could be then complemented by ADS-B from 2015), Portugal (Azores by 2011, WAM/ADS-B), Latvia (WAM by 2011), the Netherlands (N. Sea by 2011, WAM/ADS-B), Romania (WAM by 2011) and possibly Sweden (Kiruna by 2011, ADS-B only).

Other ANSPs have implementation plans with target dates of deployment from 2012-13 onwards: Bulgaria (WAM/ADS-B) Cyprus (ADS-B), France (overseas territory, ADS-B), Iceland (ADS-B), Italy (ADS-B), Greece (WAM/ADS-B), Norway (N. Sea, ADS-B), Portugal (WAM/ADS-B), Sweden (WAM/ADS-B) and UK (Scotland, WAM). In addition, UK NATS has included ADS-B with WAM in their Strategy (target date for ADS-B implementation is from 2018).

Airlines have started their certification and operational approval process for ADS-B. Several hundreds of aircraft are already certified for ADS-B operations in Non-Radar Airspace. More than 500 aircraft of them have received their EASA airworthiness certification, in the context of the CASCADE ADS-B Pioneer airline project.

Regarding the second step, the implementation based on the SPI Implementing Rule (mandate) covers SSR, Mode S and ADS-B Extended Squitter [3]. This will make airborne installations “future proof”, i.e. supporting all surveillance techniques currently used or planned to be used. The

rulemaking will require full compliance with all “ADS-B out” requirements in support of Ground and Airborne Surveillance applications.

In terms of the future ADS-B avionics requirements, the SPI IR will necessitate a transponder upgrade to ED102A/DO260B and a direct GNSS receiver-transponder wiring.

The first aircraft with certified avionics, compliant with the European Commission Implementing Rule, will be available already from late 2011 onwards. The number of aircraft which will be compliant with the Rule will be increased in the next years driven by the mandate dates.

In parallel, “ADS-B in” will be introduced operationally by the pioneer airlines supported by ISAVIA and UK NATS from 2011 onwards on a voluntary basis, driven by the benefits to be acquired. The first applications are the ATSAW during Flight Operations (ATSAW AIRB) and the ATSAW In Trail Procedure (ITP) over N. Atlantic (Shanwick FIR and Reykjavik FIR).

The issue of establishment and use of a list of aircraft that are approved to receive an operational ADS-B service within Europe will be investigated. The work will actively explore synergies with similar activities worldwide and any needs/opportunities for wider co-ordination.

VIII. CONCLUDING REMARKSThe implementation of WAM and initial ADS-B

applications (“ADS-B out” and ATSAW) is ongoing in Europe and worldwide. More ANSPs opt for a combined deployment of WAM/ADS-B, demonstrating the synergies of these two Surveillance techniques for specific operational environments.

In general, the optimal mix of the various Surveillance techniques (SSR Mode S, ADS-B, WAM) depends on the local environment, operational needs and business case from an overall ATM Network viewpoint. This will allow a smooth transition path from short term (radar like) Suveillance system in a mixed equipage environment to the future high performance, rationalised and interoperable Surveillance system.

The significant progress achieved will enable benefits for the ATM Network and establish the Surveillance foundation for the SESAR Programme work including the deployment of the future more demanding applications.

REFERENCES

[1] Status of WAM and ADS-B implementation in Europe, C. Rekkas, EUROCONTROL, ESAVS Conference, Berlin, 2010

[2] Strategic Guidance in Support for the Execution of the European ATM Master Plan, EUROCONTROL, May 2009

[3] Draft Implementing Rule on Surveillance Performance and Interoperability Requirements (SPI-IR), v3.0, 2010

[4] Safety Performance and Interoperability Requirements for ADS-B in Non Radar Airspace (ADS-B NRA), EUROCAE ED-126 / RTCA DO-303, 2006


[5] Safety Performance and Interoperability Requirements for ADS-B in Radar Airspace (ADS-B RAD), EUROCAE ED-161 / RTCA DO-318, 2009

[6] Safety Performance and Interoperability Requirements for ADS-B for Airport Surface Surveillance (ADS-B APT), EUROCAE ED-163 / DO-321, 2010

[7] Safety Performance and Interoperability Requirements for ATSAW In-Trail procedure in oceanic airspace (ATSAW ITP), EUROCAE ED-159 / RTCA DO-312, 2008

[8] Safety Performance and Interoperability Requirements for ATSAW Visual Separation in Approach (ATSAW VSA), EUROCAE ED-160 / RTCA DO-314, 2008

[9] Safety Performance and Interoperability Requirements for ATSAW during flight operations (ATSAW AIRB), EUROCAE ED-164 / DO-319, 2010

[10] Safety Performance and Interoperability Requirements for ATSAW on the Airport Surface (ATSAW SURF), EUROCAE ED-165 / RTCA DO-322, 2010

[11] Safety Performance and Interoperability Requirements for Flight Deck Interval Management (ASPA-FIM), EUROCAE ED-195, 2011

[12] Acceptable Means of Compliance for ADS-B NRA (AMC 20-24),EASA, 2008

[13] Technical Specification for the ADS-B Ground Station, ED-129, EUROCAE, 2010

[14] Minimum Operational Performance Standards for 1090 MHz ADS-B and TIS-B, EUROCAE ED-102A / RTCA DO260B, 2009

[15] Technical Specification for WAM system, , EUROCAE ED-142, October 2009

[16] Preliminary Safety Case for ADS-B NRA, EUROCONTROL, 2008[17] Preliminary Safety Case for ADS-B RAD, EUROCONTROL, 2011


U.S. Activities in ADS-B Systems Implementation

P. Douglas Arbuckle SBS Program / Joint Planning & Development Office

US Federal Aviation Administration [email protected]

Abstract—This paper summarizes current U.S. activities and future plans in ADS-B System Implementation. The services provided by FAA and delivery approach are described, followed by the FAA development strategy and status. The paper describes actions being taken by FAA to encourage ADS-B equipage. The paper concludes with discussion of preparations for future FAA ADS-B activities.

Keywords-ADS-B, US FAA, implementation, avionics, ATC

I. PROVIDED SERVICES & SUPPORTING CAPABILITIES

The U.S. Federal Aviation Administration (FAA) is delivering Surveillance and Broadcast Services (SBS) as described in this section. SBS services are provided via a set of FAA-specified service volumes in en route airspace, terminal area airspace, and on airport surfaces.

A. ADS-B Fig. 11 shows the ADS-B service architecture. Aircraft with

Version 2 avionics certified per FAA Advisory Circular (AC) 20-165 [1] (or an equivalent approved by FAA Aircraft Certification) will receive ATC separation service in the U.S.2The U.S. is supporting two ADS-B links:

• the 978 MHz Universal Access Transceiver (UAT) link per FAA Technical Standard Order (TSO)-C154c [2];

• the 1090 MHz Extended Squitter (1090ES) link per TSO-C166b [3].

The U.S. ADS-B Final Rule will require aircraft that operate above FL180 to broadcast on the 1090ES link [4]. The FAA is not prescribing the choice of link for aircraft flying below FL180; both links are supported and operators are free to choose whichever link meets their needs. Aircraft broadcasts go to other aircraft and to ground radio stations, where the information is processed and displayed to controllers. Where available, information from FAA radars is combined with ADS-B data to support ATC separation services.

Aircraft with ADS-B-In capability directly receive aircraft broadcasts on the same link around them, limited in range only by line-of-sight or received signal strength. Aircraft on the same link or aircraft capable of receiving on both links have no

1Fig. 1 through Fig. 4 are copyright 2007, ITT Corp., and used by permission. 2Specifically-approved aircraft equipped with Version 1 avionics are currently receiving ADS-B-only ATC separation services in Alaska and the Gulf of Mexico.

need to receive traffic information from the FAA-provided service described in section I.B. Aircraft broadcasting on one link (example: UAT) are not received by aircraft using only the other link (example: 1090ES) and vice/versa, which justifies the service described in I.B.

On 28 May 2010, the U.S. ADS-B Final Rule was published, requiring ADS-B Out equipage in U.S. airspace where a transponder is currently required, with compliance required by 1 Jan 2020. The U.S. ADS-B Final Rule also specifies requirements for broadcast information, including minimum thresholds for position/velocity accuracy and integrity [4].

Figure 1. ADS-B Service Architecture

B. ADS-Rebroadcast Fig. 2 shows the ADS-Rebroadcast (ADS-R) service

architecture. ADS-R is a pilot advisory service that receives data from aircraft on one link and immediately rebroadcasts it on the other link. To conserve spectrum, the service identifies aircraft broadcasting that they are ADS-B-In equipped as "client" aircraft. The traffic broadcasting on the other link within a specified radius and altitude band around each client aircraft are then rebroadcast on the client’s link via ADS-R. Note that ADS-R services are only available when both aircraft are within range of any ground radio station. Since ADS-B ground stations are sited to cover current radar airspace, this means that there will be regions of airspace (typically at lower altitudes) without ADS-R coverage. Various avionics manufacturers are considering the market opportunities for ADS-B avionics with dual-link receive capability.


Figure 2. ADS-R Service Architecture

C. Traffic Information Service - Broadcast Fig. 3 shows the Traffic Information Service - Broadcast

(TIS-B) service architecture. TIS-B is a pilot advisory service for situation awareness, gathering data from U.S. ATC radars, Wide Area Multilateration (WAM) systems such as those used in Alaska/Colorado, and surface multilateration systems like ASDE-X. This non-ADS-B surveillance information is broadcast as a TIS-B service through ground radio stations to participating aircraft on both links. Like ADS-R, appropriately equipped aircraft are identified as client aircraft and non-ADS-B traffic within a specified radius and altitude band around the client aircraft are selected for TIS-B. Unlike ADS-R, TIS-B messages are structured so that information about multiple aircraft can be packaged into a single TIS-B broadcast.

Figure 3. TIS-B Service Architecture

D. Flight Information Service - Broadcast Fig. 4 shows the Flight Information Service - Broadcast

(FIS-B) service architecture. FIS-B is a pilot advisory service supported by the FAA that is only broadcast on the UAT link. The FIS-B message set contains Airman's Meteorological Information, Aviation Routine Weather Report (METAR) and Unscheduled Specials, Next Generation Radar (NEXRAD) precipitation reflectivity, Pilot Reports (urgent and routine), Significant Meteorological Information, Terminal Area Forecast and unscheduled Amendments, Winds and Temperatures Aloft, Notices to Airmen (NOTAMs) important to flight safety, and Status of Special Use Airspace.

The FAA is considering additional products for the FIS-B service in the future. Products under consideration include Echo tops, Lightning strikes, Severe Weather Forecast Alerts and Severe Weather Watch Bulletin, Ceilings, Digital Automated Terminal Information Service, Icing (Current/Forecast Potential), Terminal Weather Information for Pilots, and Turbulence.

Figure 4. FIS-B Service Architecture

E. Service Delivery Approach and Implementation Status ITT is the prime contractor selected by the FAA under a

service contract to provide surveillance and broadcast services. The ITT ground radio infrastructure receives/transmits messages from either Version 1 or 2 avionics. The ITT infrastructure also receives messages from Version 0 avionics, but does not transmit TIS-B/ADS-R uplink messages in Version 0 format. At a point prior to 2020, ground station transmission of TIS-B/ADS-R/FIS-B messages in the Version 1 format will be discontinued.

As of 1 Jun 2011, 349 radio sites of over 700 planned sites were constructed and 280 radio sites had been declared operational by the FAA. See Fig. 5 3 for a map of the operational radios as of 1 Jun 2011; the latest map can be found on the FAA website [5].

Figure 5. U.S. ADS-B Implementation Status as of 1 Jun 2011

3 Fig. 5 through Fig. 7 are in the public domain. No rights reserved or conveyed.


F. SBS Monitor The SBS Monitor provides an FAA-developed independent

monitor of the status of the ITT ground infrastructure and provides an assessment of ITT’s performance in delivering services to the FAA. The SBS Monitor performs or will perform the following functions:

1) Contract Technical Performance Monitoring Provide an independent assessment of the Technical

Performance Measures (TPMs) for the ADS-B services delivered to FAA. These metrics nominally include the latency, availability, and update rate of the SBS services provided by ITT. The Contracting Officer uses the TPMs to evaluate the quality of the SBS services provided by ITT.

2) Avionics Compliance Monitoring Monitor aircraft ADS-B reports to measure equipage levels,

characterize duplicate/invalid International Civil Aviation Organization (ICAO) address issues, and evaluate compliance with avionics performance requirements defined in Title 14 of the Code of Federal Regulation (14 CFR) §91.227.

3) Service Status Monitoring Inform FAA Operations Control Centers, who then notify

users via NOTAMs, about of the current status of SBS services throughout the U.S.

II. FAA DEVELOPMENT STRATEGY

Fig. 6 shows the overall FAA ADS-B development strategy for 2011-2015. ATC Separation Services will be rolled out on a facility-by-facility basis by declaring Initial Operational Capability (IOC) at each site. Major facilities will achieve IOC by the end of 2013, but rollout will continue at some smaller terminal facilities until 2015, due to the need for automation system modernization at those facilities.

ATC Surface Advisory Services refer to ADS-B services provided by FAA at those locations where surface surveillance systems exist, which include both the Airport Surface Detection Equipment, Model X (ASDE-X) and the new Airport Surface Surveillance Capability (ASSC) [6] that is currently under development.

Figure 6. FAA ADS-B Development Strategy

A. ATC Separation Services Since late 2009, the FAA has been delivering ATC

separation services to aircraft equipped with ADS-B Version 1 avionics (TSO-C154b/TSO-C166a). Between 2009 and 2010, the following key-sites achieved IOC:

• Louisville Terminal Radar Approach Control (TRACON);

• Houston Air Route Traffic Control Center (ZHU) Gulf of Mexico airspace;

• Philadelphia TRACON;

• airspace in the vicinity of Juneau, Alaska.

For both TRACONs, ATC separation services have been provided using fused radar and ADS-B.

Based on this operational experience, FAA made an In-Service Decision (ISD) for Surveillance and Broadcast Services on 22 Sep 2010, indicating that the use of ADS-B and Wide Area Multilateration (WAM) are operationally suitable as surveillance sources for ATC Separation Services in the United States. As with any complex system, there were a set of issues raised during the testing and evaluation phase that are being addressed going forward. These issues, documented in ISD Action Plans, are being resolved as needed to enable activation of ADS-B for ATC Separation Services in the initial production sites.

The initial terminal production sites are Houston TRACON for the Standard Terminal Automation Replacement System (STARS) and New York TRACON for the Common Automated Radar Terminal System (CARTS). The activities that are being completed for CARTS and STARS include updating the software baselines to support ATC terminal separation for ADS-B-only targets (for aircraft equipped with Version 2 avionics). End-to-end system testing is being conducted to validate the separation standards analyses for ADS-B-to-ADS-B and ADS-B-to-radar separation services.

The initial En Route Automation Modernization (ERAM) production site is ZHU. ZHU will implement ADS-B data integration with ERAM in phases. The first phase will provide ADS-B data to ERAM via a "virtual radar" interface now being used by the ZHU En Route Host system to provide separation services in the Gulf of Mexico airspace. In the second phase, ERAM will be provided with ADS-B data to enable ATC separation services using a fused ADS-B and radar picture that will be used for additional Centers after ZHU.

By September 2012, FAA plans to integrate ADS-B surveillance data in the Advanced Technologies and Oceanic Procedures (ATOP) automation platform to support ATC separation services in oceanic airspace for which the U.S. is responsible.

B. Pilot Advisory Services The continued deployment of Pilot Advisory Services

(broadcast of TIS-B/ADS-R and FIS-B) continues. As of 1 Jun 2011, Pilot Advisory Services were operational in the following Service Volumes: Boston Center, New York Center, Cleveland Center, Chicago Center, Washington Center, Atlanta


Center, Jacksonville Center, Miami Center4 , Seattle Center, Oakland Center, Los Angeles Center, Albuquerque Center, Minneapolis Center, the Gulf of Mexico, Louisville surface & terminal, Newburgh/LaGuardia/Newark/JFK/Islip terminal, Bradley-Windsor Locks terminal, Philadelphia surface & terminal, Pittsburgh terminal, Miami terminal, Ft Lauderdale terminal, Gainesville terminal, Jacksonville terminal, Savannah terminal, the Anchorage-Fairbanks en route corridor, Juneau and the surrounding area in Southeast Alaska. The latest updated information on FAA surveillance and broadcast service deployment can be found on the FAA website [5].

C. Pilot Applications The FAA is developing a number of pilot applications that

are expected to provide benefits to operators who choose to equip their aircraft with appropriate ADS-B-In avionics which provide the capability to receive, process, and display ADS-B and TIS-B data from surrounding aircraft. In addition to providing benefits directly to customers who equip, these applications will help accelerate the understanding and acceptance of ADS-B and provide a path to future applications.

The FAA is currently investing in development and flight testing/trials for three applications: Flight-deck-based Interval Management - Spacing (FIM-S), Oceanic In-Trail Procedures (ITP), and Traffic Situation Awareness with Alerts (TSAA). Fig. 7 shows the plans for these applications as part of the FAA ADS-B Strategy (Fig. 6).

Figure 7. Pilot Application Development Strategy

1) In-Trail Procedures (ITP) The objective of ITP is to increase the efficiency of long-

haul flights in non-surveillance airspace while maintaining or enhancing the current level of safety. The concept takes advantage of ADS-B In to display traffic on a cockpit display of traffic information. In addition to increasing flight crew awareness of surrounding traffic, the ITP capability will enable climbs or descents to more optimal operational altitudes currently blocked by traffic due to procedural separation standards. There is an expectation that once flight crews gain experience with the onboard ITP system and procedures, they

4 TIS-B and FIS-B only as of 1 Jun 2011; ADS-R will be activated by 2012

will reduce their discretionary fuel reserves, further reducing fuel burn (and carbon emissions) and potentially allowing more payload for cargo.

The FAA and United Airlines plan to conduct operational evaluations of ADS-B ITP on 12 United B747-400 aircraft on routes between the U.S. west coast and Australia beginning by August 2011, and subsequently in other suitable oceanic airspace managed by Oakland Center. The FAA has developed ITP Interim Policy [7] to support certification of the ITP functionality in various aircraft until TSO-C195a is published and appropriate ACs are modified.

2) Flight-deck-based Interval Management Interval Management (IM) introduces a new method for

flight crews and air traffic control to achieve a desired spacing between aircraft in all phases of flight. The initial applications of these operations will take place for arriving aircraft in en route airspace to a terminal area metering fix consistent with today's IFR procedures and criteria. Later implementations of these operations include the possibility of having the flight deck execute an interval management delegated separation clearance issued by ATC.

IM operations consist of a ground capability called Ground Interval Management - Spacing (GIM-S) to schedule/manage the arrival traffic flow, and a flight deck capability (FIM-S) to allow the aircraft to efficiently manage the interval assigned by air traffic control. The FAA is implementing the requirements for the capabilities in GIM-S via two FAA automation programs: Time-Based Flow Management (TBFM) and ERAM.

The FAA has several airline partners prepared to support operational data collection and benefits measurement as the initial FIM-S capabilities are established. The FAA supported the efforts of a joint RTCA/EUROCAE working group to develop the Safety, Performance and interoperability Requirements (SPR) document for FIM-S (also known as ASPA-IM). This effort will conclude with RTCA and EUROCAE approval of the FIM-S SPR in the summer of 2011.

3) Traffic Situation Awareness with Alerts Traffic Situation Awareness with Alerts (TSAA) is aimed

at improving a pilot’s identification of conflicting traffic by providing onboard alerts for aircraft without Traffic Alert and Collision Avoidance System (TCAS) equipment. Such traffic may or may not have been pointed out by air traffic control. This alert identifies conflicting traffic, but does not provide any resolution maneuver advice. TSAA will be tailored to operate without excessive nuisance alerts when operated in the VFR traffic pattern at small general aviation airports, where most collision accidents occur. In the airport environment, the ownship can receive an alert while still on the ground for a projected conflict at a future point where both aircraft will be airborne.

The FAA has recently initiated work with the Massachusetts Institute of Technology (MIT) (and Avidyne as MIT's subcontractor) to develop this application. The FAA has engaged the Aircraft Owners and Pilots Association, the General Aviation Manufacturers Association, and Helicopter


Association International stakeholders to participate with the FAA in periodic reviews of the MIT/Avidyne work. A proposed concept of operations was provided to a joint RTCA/EUROCAE working group in May 2011. The goal of this activity is to work through RTCA/EUROCAE to produce Minimum Operational Performance Standards material for this application by mid-2013.

4) Current ADS-B-In Standards With the publication of TSO-C195 [8] and AC 20-172 [9],

the FAA has essentially completed avionics certification guidance for Traffic Situation Awareness - Basic, Traffic Situation Awareness for Visual Approach, and Airport Traffic Situation Awareness. RTCA and EUROCAE are working to harmonize avionics standards for these applications, plus the ITP application, with a plan to complete this work by December 2011. The resulting RTCA/EUROCAE harmonized standards will serve as the basis for TSO-C195a.

III. ENCOURAGING ADS-B EQUIPAGE

A. Using ADS-B to Enhance ATC Separation Services As a means of encouraging early ADS-B Out equipage, the

FAA is exploring opportunities to use ADS-B surveillance coverage to improve airspace access, enable more direct routings and more fuel-efficient altitudes, and circumvent constrained airspace. Currently the focus is on offshore/oceanic airspace near the coasts of the U.S. mainland and Alaska. However, other opportunities exist in the Caribbean as well as at low altitudes in Alaska and the mountainous regions of the western U.S. What all of these regions have in common is either a lack of radar coverage or relatively unreliable radar coverage.

The FAA recently signed an agreement with JetBlue to explore the benefits of ADS-B surveillance in offshore airspace along the U.S. east coast. The FAA is discussing opportunities to explore benefits in similar-type airspace with other airlines/operators.

B. Vehicle ADS-B The FAA is promoting ADS-B for use with vehicles on

airport surfaces to improve runway safety. Any vehicle (e.g., a tug, fuel truck, snowplow, or rescue-and-firefighting vehicle) can be equipped to transmit location information to controllers, pilots, vehicle drivers, or airport operators. In the U.S., ADS-B transmissions will only be permitted from ground vehicles that are in the airport movement area (and subject to air traffic control).

While not mandating vehicle ADS-B, the FAA is encouraging airport operators to equip appropriate vehicles. In addition to significant improvements in runway safety, airport managers could use ADS-B information to track assets more efficiently. This would be especially useful with rescue vehicles in case of an accident.

The FAA has issued a draft AC for Vehicle ADS-B Equipment [10]. The final AC, when issued, will help airport managers understand how to determine which vehicle transponders meet FAA performance requirements, inform the

FAA of the airport's intent to proceed with vehicle ADS-B, request unique ICAO identifying numbers for vehicles to be equipped, and request a transmit license.

C. 3 Nautical Mile Separation in En Route Airspace The FAA has begun work to analyze the target level of

safety for an ADS-B application that would enable 3-nautical mile (NM) ATC separation in domestic en route airspace where it is currently not permitted (in the U.S., 3-NM separation can only be applied below certain altitudes and under certain conditions). The FAA plans to complete the initial performance and safety assessments for this application by September 2011.

D. Avionics Upgrades to Version 2 Avionics The FAA is working with partners who were early adopters

of ADS-B to upgrade those avionics (Version 1) to the avionics standards (Version 2) [2, 3] required by the U.S. ADS-B Final Rule [4]. Specifically, these partners are UPS, US Airways, operators in Alaska equipped with avionics under the FAA Capstone Program, and several helicopter operators in the Gulf of Mexico. Currently, the FAA is funding the upgrade from Version 1 to Version 2 transponders for the UPS fleet and the US Airways A330 fleet. As part of this effort, ACSS is one of the first applicants to exercise the provisions of AC 20-165 [1]. In the next 2-3 years, FAA plans to engage with Alaska operators with Capstone equipment and the Gulf of Mexico helicopter operators to assist them in upgrading their Version 1 avionics to Version 2, so that they can comply with AC 20-165 and the U.S. ADS-B Final Rule.

IV. PREPARING FOR THE FUTURE

A. ADS-B Service Availability Prediction Tool The ADS-B Service Availability Prediction Tool (SAPT) is

being developed pursuant to an ADS-B Aviation Rulemaking Committee (ARC) Recommendation [11]. The ARC’s concern centered on the difficulty a user would have in predicting the expected availability of a given Global Positioning System (GPS) accuracy/integrity performance level over a planned route of flight.

The SAPT assumes the minimum performance requirements for Global Navigation Satellite System (GNSS) sensors, as required in the appropriate TSOs. The SAPT prediction is based upon; (1) the time, route and airspace of the planned flight; (2) ADS-B-related avionics on the subject aircraft; and (3) the announced status of the GPS satellite constellation.

The SAPT will be one method for an operator to assess the availability of required ADS-B performance for a flight. Operators also may choose to use an alternative FAA-approved prediction tool.

B. Wide-Area Multilateration The FAA has deployed Wide-Area Multilateration (WAM)

in Juneau, Alaska, and several airports in Colorado. The terrain in these regions makes it impossible for air traffic controllers to maintain radar surveillance over aircraft at lower altitudes.


Limitations to aircraft operations are compounded by bad weather that results in flight delays and cancellations. WAM reached IOC at Denver Center in September 2009. The WAM system at Anchorage Center, which services Juneau, reached IOC in January 2010.

The FAA is working with the State of Colorado to deploy a new, integrated WAM/ADS-B system that will fall under the FAA service contract with ITT. Currently, IOC of this system in western Colorado is targeted for 2013. This integrated WAM/ADS-B system will become the new standard configuration for the future deployment of other such systems in the U.S.

C. Surface Multipath Issue The FAA funded multiple activities to support the

development of an SPR for the surface situation awareness with indications and alerts (SURF-IA) application [12]. As part of this, Honeywell and ACSS conducted demonstrations of SURF-IA application prototypes in the Seattle area and the Philadelphia area. These demonstrations identified performance issues with ADS-B-In equipment being able to consistently detect other ADS-B aircraft that were operating on the surface movement area and within line-of-sight.

The FAA subsequently collected surface ADS-B data at Philadelphia International Airport (PHL) from a dedicated test event in October 2010 and determined that multipath was interfering with the incident ADS-B signals at multiple locations on the airport surface. As observed in the test results, the multipath signaling phenomenon effectively creates "blind spots" at which ADS-B aircraft could not be seen with ADS-B-In systems.

The FAA is currently working to characterize these multipath effects at other airports to help determine the full extent of this problem, as well as conduct bench tests of ADS-B-In receivers from multiple manufacturers to assess the impact of multipath on these systems. Once the FAA has a better understanding of this phenomenon, then FAA can develop potential mitigations and evaluate alternatives in consultation with the industry and other nations.

D. ADS-B-In Aviation Rulemaking Committee The ADS-B Aviation Rulemaking Committee (ARC)

recommended that the FAA, in partnership with industry, should define a strategy for ADS-B In by 2012, ensuring the strategy is compatible with ADS-B Out avionics [11]. The committee further recommended that the FAA define how to proceed with ADS-B In beyond the voluntary equipage concept in the U.S. ADS-B Notice of Proposed Rulemaking. In response to this recommendation, the FAA chartered an ADS-B-In ARC on 30 June 2010, with the following requested deliverables.

Deliverable Due Date Task 1: ARC endorsement (or not) of continuing near-term application development

Oct 2010

Task 2: Final ARC ADS-B-In strategy recommendations 30 Sep 2011 Task 3: Delivery of products from any activities that follow up ADS-B-In strategy recommendations

1 Jun 2012

On 1 November 2010, the ADS-B-In ARC sent its Task 1 recommendations to the FAA. The ADS-B-In ARC recommended a strategy where avionics standards are developed for Flight-deck-based Interval Management – Delegated Separation (FIM-DS), but are initially used to perform FIM-S operations until enough confidence is gained to enable FIM-DS operations to begin. The ADS-B-In ARC is engaged in activities to develop its Task 2 report outlining recommendations to the FAA on an ADS-B-In development strategy.

E. SBS Program Next Phase In early 2012, the FAA will conduct an internal review of

the next phase of the Surveillance and Broadcast Services program (Fiscal Years 2014-2020). Inputs to this review will come from the ADS-B-In ARC report, results of the above studies, prior commitments of the FAA, and the overall FAA budget environment. During this review, senior FAA leadership will determine what ADS-B-related work beyond the currently planned scope will be pursued by FAA in the 2014-2020 timeframe.

REFERENCES

[1] FAA AC 20-165 (ADS-B Out Installation Guidance), http://www.faa.gov/documentLibrary/media/Advisory_Circular/AC%2020-165.pdf

[2] FAA TSO-C154c (UAT Link), http://rgl.faa.gov/Regulatory_and_Guidance_Library/rgTSO.nsf/0/e5a37977fbdb786b8625768200579728/$FILE/TSO-154c.pdf

[3] FAA TSO-C166b (1090ES Link), http://rgl.faa.gov/Regulatory_and_Guidance_Library/rgTSO.nsf/0/e70544d62a001f87862576820057970f/$FILE/TSO-166b.pdf

[4] U.S. ADS-B Final Rule, http://www.regulations.gov/#!documentDetail;D=FAA-2007-29305-0289

[5] NextGen Technologies Interactive Map, http://www.faa.gov/nextgen/flashmap

[6] Airport Surface Surveillance Capability Market Survey, faaco.faa.gov/attachments/Airport_Surface_Surveillance_System_Market_Survey_020110.doc

[7] FAA ITP Interim Policy (Certification and Flight Standards), http://rgl.faa.gov/Regulatory_and_Guidance_Library/rgPolicy.nsf/0/4052aba0492da8ca86257726005dba3e/$FILE/ITP%20Interim%20Policy.pdf

[8] FAA TSO-C195 (Aircraft Surveillance Applications), http://rgl.faa.gov/Regulatory_and_Guidance_Library/rgTSO.nsf/0/17a0f0ab5a874c55862577ad0052a38e/$FILE/TSO-C195.pdf

[9] FAA AC 20-172 (ADS-B In Installation Guidance), http://www.faa.gov/documentLibrary/media/Advisory_Circular/AC%2020-172.pdf

[10] FAA DRAFT Advisory Circular for Vehicle ADS-B, http://www.faa.gov/documentLibrary/media/Advisory_Circular/draft_150_5220_xx_ads_b.pdf

[11] Report from the ADS-B Aviation Rulemaking Committee to the FAA, 26-Sep-2008, http://www.airlines.org/SafetyOps/FlightOperations/Documents/ARCRecommendationstotheADSBNPRM.pdf

[12] DO-323, Safety, Performance and Interoperability Requirements Document for Enhanced Traffic Situational Awareness on the Airport Surface with Indications and Alerts (SURF IA), available from RTCA.


Detect and avoid for Unmanned Aircraft Systems in the total system approach

Filippo Tomasello & David Haddon Rulemaking Directorate

European Aviation Safety Agency (EASA) Cologne (Germany)

[email protected] & [email protected]

Abstract— ICAO requires aircraft not to be in hazardous proximity. Pilots shall avert collisions. In controlled airspace separations are established by Air Traffic Control (ATC), but pilot responsibilities remain. Present ICAO wording may not be sufficient for Unmanned Aircraft Systems (UAS), since the Detect and Avoid (D&A) needs quantitatively defined parameters.

Keywords (ICAO, UAS, Detect and Avoid, Total system approach)

I. INTRODUCTION

The International Civil Aviation Organisation (ICAO), aware of the progressive emergence of Unmanned Aircraft Systems (UAS) for civil use, has already set up in 2007 a specific Study Group (UAS SG), to develop international provisions in this field. The Study Group has already supported the ICAO Secretariat in developing an ICAO Circular [1] on the matter.

Therein it is clearly stated that UAS are indeed aircraft and, as such, when involved in international civil aviation, they are in principle subject to all the applicable standards, as published in the Annexes to the Chicago Convention.

However, neither the Circular is a binding document, nor the present text of the Annexes to the Chicago Convention had been written considering both manned and unmanned aviation. More work therefore needs to be done.

The 37th General Assembly of ICAO, in October 2010, therefore endorsed [2] the development of a comprehensive international regulatory framework for civil UAS. This will culminate, possibly between 2015 and 2020, with adoption of newly developed or amended ICAO Standards and Recommended Practices (SARPs), in order to support the safe integration of UAS into non-segregated airspace.

The work is already underway and in fact ICAO has already amended Annex 13, in 2010, to clarify that also UAS accidents have to be investigated. The amendment of Annex 2, which is underway, is described below, while the UAS SG envisages proposing an ICAO “UAS Manual” (or a series of Manuals), in 2013-14, to pave the way for the future standards.

II. RULES OF THE AIR

The first SARPs to be considered are the Rules of the Air, contained in Annex 2 to the Chicago Convention. These were first published in September 1948 when, at least for civil aviation, the idea of “unmanned” aircraft was not yet considered relevant (although UAS ante litteram had already been built before the First World War). Nevertheless the Annex today applies to all aircraft (with or without a pilot on board; i.e. “manned” or “unmanned”).

Therein ICAO requires [3] in particular that all aircraft are not operated in such proximity to other aircraft as to create a collision hazard.

Paragraph 3.2 in the same ICAO Annex 2 requires the Pilot-in-Command (PiC), whether on board or remote from the aircraft, to take action in order to best avert collisions. In other paragraphs of same Annex the pilot (when crossing, overtaking or similar) is required to stay “well clear” of other traffic.

Furthermore Annex 2 already contains a specific Appendix to facilitate the “special authorization” (ref. article 8 of the Chicago Convention) required to fly unmanned free balloons.

In May 2011, the ICAO Secretariat, supported by the mentioned UAS SG, has presented to the Air Navigation Commission (ANC) a proposal for amendment of Annex 2, essentially centred on a new Appendix, aiming at facilitating the “special authorization” not only for balloons, but also for more complex types of UAS, like aeroplanes or rotorcraft. Such a proposal is accompanied by amendments to Annex 7 to specify that all aircraft types known today can indeed be “manned” or “unmanned”.

In these proposals UAS are considered to be possibly “automated” (i.e. no human intervention possible) or “Remotely Piloted”. Only Remotely Piloted Aircraft (RPA) are considered eligible to cross national borders.

In general terms the proposals clarify that RPA shall be operated (par. 3.1.9) in such a manner as to minimise hazards to persons, property or other aircraft. This statement of principle is important, because it shifts the emphasis from


protection of passengers on board, which is appropriate for traditional manned aviation, to protection of third parties on the ground or in the air, since there are no humans on board of RPA. But of course the absence of humans on board does not mean that there are no risks for third parties.

Airworthiness provisions, out of scope of this paper, focus on protection of third parties on the ground. Herein the attention is instead called on protection of third parties in the air: i.e. other airspace users.

Minimising risks for other (manned) aircraft means to have appropriate tools to remain “well clear” of other traffic and, as last resort, to avoid collisions.

Two operational scenarios are envisaged. In Visual Line of Sight (VLOS) the RPA, and the airspace surrounding it, is visible from the position of the ground crew. The latter is therefore responsible for “see and avoid” similarly to manned aircraft. Neither automation is necessary on board, nor is a quantitative definition of “well clear” required. These operations, typically with RPA well below 150 Kg, are already creeping today. Of course VLOS operations are not only limited by visibility, but also by range (typically 400 ft of maximum height and 500 m of maximum distance from crew) in order to take into account the perception capabilities of the human eyes.

To go Beyond VLOS (i.e. BVLOS) it is necessary to rely on on-board sensors and systems for “detect and avoid” (D&A) as well as the capability to establish communications with ATC, including any alternate means of communications (in fact being the Remote Pilot Station on the ground, several communication architectures are possible).

The majority of experts concur today that BVLOS operations may be easier under Instrument Flight Rules (IFR) than under Visual Flight Rules (VFR). On the other side, RPA need modern automated navigation systems on board and so, for them, flying IFR is in principle not a major problem.

Besides D&A, it is important to note that the proposed amendments to Annex 2 and 7, building upon the fact that UAS are considered “aircraft”, clarify that:

• Each RPA needs an individual Certificate of Airworthiness (CofA) and registration;

• Each RPA operator (which is normally an organisation, not a natural person) needs an RPA Operator Certificate (ROC), similar to the Air Operator Certificate (AOC) for operators of Commercial Air Transport (CAT) using manned aircraft;

• Remote Pilots shall have a specific Licence (RPL), the standards for which will be included in ICAO Annex 1.

In other words, the ICAO approach is in harmony with recital (1) of the EASA Basic Regulation [4], which invites to consider the “total system” perspective when regulating aviation safety: i.e. not only airworthiness of the “machine”,

but all the related safety provisions, of course starting from licensed pilots and certified air operators.

In the aviation tradition, ICAO Annex 2 only establishes high level principles, which then need to be more detailed at ICAO (e.g. in other Annexes), regional (e.g. EASA rules) or national level. It is however extremely important that Annex 2 will be amended, because this will legitimate civilian RPA as user of the non-segregated airspace under General Air Traffic (GAT) rules.

Should the Air Navigation Commission (ANC) of ICAO in principle accept the proposals from the Secretariat, ICAO Contracting States and international organisations could be consulted in 2011. In case of favourable consultation results, the amended SARPs could become applicable at end of 2012.

Consequently, since Annex 2 will only establish the principle that D&A is necessary to fly BVOLS, much more details are necessary to define a safe and feasible D&A system.

III. THE NEED FOR QUANTIFICATION

The existing ICAO standards in Annex 2, in relation to prevention of collisions, are often referred to as “see and avoid”, although this expression is not explicitly used in said ICAO Annex. “See and avoid” of course implies the perception by a human on board and his/her judgment, based on a qualitative assessment, which is in the capabilities of the humans.

When the “Remote Pilot” is on the ground, as in the case of Remotely Piloted Aircraft (RPA), a “Detect and Avoid” (D&A) function is necessary on board in the case of BVLOS operations. The functions could include different purposes (e.g. downlink information for “situational awareness”, maintain distance from clouds, “see” aerodrome visual aids, etc.), but also downlink alerts when pilot’s decision would be required to establish and maintain a safe distance.

Most probably advanced automation would be required in case of imminent risk of collision: i.e. the RPA will initiate the averting manoeuvre and inform the remote pilot. The latter will have override authority. This is the case today with on board autopilots.

In any case D&A needs quantitative parameters to be defined. Today the majority of experts tend towards three thresholds:

• A first one, relevant for “traffic avoidance”, which will alert the pilot that there is an imminent risk of coming “too close” to an “intruder” aircraft;

• The second for “collision avoidance” when the risk of collision is imminent (i.e. all other safety barriers have failed), typically in a time horizon of about 30 seconds; and

• A “collision volume” which is never to be entered, since infringing its boundaries means that only providence could avoid collision.


The latter threshold is hence only relevant for designing the D&A while taking into account the RPA performances (e.g. rate of climb, descent or of turn), but not to be used in actual operations.

For the other two, quantification is necessary, since for any algorithm it is difficult to judge whether the RPA stays “well clear” or not. The debate has not yet produced firm numerical figures, but it has pointed out that possibly the threshold have to be also calculated in time units: e.g. seconds or minutes before the risk of entering the collision volume, if no evasive manoeuvre were initiated. This implies considering the geometry of the encounter as well as the speed of the RPA and of the intruder.

The calculations shall also provide the range in which the airborne sensors shall be suitable. Such a range could in particular change significantly, whether the intruder is at supersonic speed or not. Excluding the former case will lead to less demanding requirements for the airborne sensors, but most probably to operational limitations, in order to remain segregated (or at least well separated vertically) from supersonic traffic. Once the required range for the on-board sensors has been defined, “situational awareness” (i.e. down linking to the Remote Pilot information on the position of traffic nearby) could be provided, even in respect of nearby traffic which does not constitute a potential hazard of conflict. This desirable feature could allow the Remote Pilot to plan the trajectory of its RPA in the optimal way.

IV. THE OPERATIONAL CONCEPT

The operational concept is hence centred on the events that occur once the RPA trespasses the “traffic avoidance” or the “collision avoidance” thresholds.

When the first threshold is reached, the airborne sensors and system, via the “command and control” (C2) data link, will inform the Remote Pilot. She/he has sufficient time to plan the evasive manoeuvre. The first option could be to reduce speed, without changing heading or altitude. In non-segregated but non-controlled airspace (e.g. ICAO Class G) the Remote Pilot can adjust the speed of the RPA as she/he wishes, only constrained by the performances of the aircraft. In controlled airspace (i.e. ICAO Classes A to E), since flying under IFR, she/he will have to respect the last received ATC clearance. Today most of those clearances are 3D (i.e. towards a way point known in terms of Latitude and Longitude to be overflow at a certain Flight Level). Therefore, if the Remote Pilot will adjust the speed by less than 5%, most probably no contact with ATC will be necessary. The Pilot may however want to consult ATC, since the latter may know the “intentions” of the intruder, or because the speed adjustment could be more significant.

Only slightly reducing the speed is the best option since it does not change the planned trajectory. Neither it leads to greater cost (no more miles and slight reduction of fuel consumption), not to detrimental environmental effects.

This option, changing the estimated time for over flight of the next way point, is furthermore compatible with the NextGen and SESAR concept for 4D trajectory

management. In fact, even if no interaction with ATC could be necessary before adjusting speed, the on-board Flight Management System (FMS) could thereafter recalculate the expect 4D trajectory and down link it, not only to the Remote Pilot, but also to the responsible ATS Unit.

Should a simple adjustment of the speed not be deemed appropriate, the Remote Pilot could opt to maintain course and speed, since its RPA has priority on the intruder. Time and radio coverage allowing, coordination through ATC or FIS could be desirable in order to confirm that the pilot of the intruder is aware that she/he has to manoeuvre to give priority.

If on the contrary, the RPA has to give priority, before changing altitude a slight heading adjustment, in order to pass behind and “well clear” of the intruder, could be an option. Again, even in controlled airspace, if the change of heading will not lead to a significant deviation from the cleared path (e.g. an airway of defined width) no prior authorization from ATC is required.

Finally the option of climbing or descending could also be viable. In this case the risk of wake vortex has to be considered. Gain if the deviation is of only 2-300 ft, it may not be necessary to receive prior authorization from ATC.

The last case, in controlled airspace, is when the Remote Pilot decides that a manoeuvre will infringe the last received ATC clearance. In this case the Remote Pilot should, time allowing, contact ATC and obtain a new clearance. Should the Pilot feel the need to initiate immediately the manoeuvre, she/he can take this responsibility regardless of the ATC clearance, but, in controlled airspace, she/he should, immediately after, inform the ATC Unit.

Once the second threshold (i.e. risk of imminent collision) is trespassed, the RPA, like in case of ACAS on board large manned aeroplanes, should react immediately and initiate the escape manoeuvre regardless of any ATC clearance. In controlled airspace, like for manned aviation, ATC has to be informed thereafter as soon as possible.

On current large aeroplanes, when the ACAS issues a “Resolution Advisory” (RA), an immediate commend by the pilot in the cockpit is necessary to comply. However some recent aircraft types (e.g. A-380) already have the possibility of coupling the RA with the autopilot: in this case the aircraft will automatically initiate the escape manoeuvre, while the pilot retains override authority.

The authors of the present paper believe that this should be the solution for UAS, also considering that, should a decision by the Remote Pilot be necessary, this will follow a transaction through the C2 data link, which means additional time (latency), especially in case of C2 via SATCOM. But, perhaps even more important, the continuity of the data link is influenced by many factors (e.g. RPA attitude, geographical position, interferences or atmospheric phenomena) and therefore the risk that the C2 may not be available at the time required exist.

In summary manned aircraft can fly following Visual (VFR) or Instrument Flight Rules (IFR) in controlled or non-


controlled airspace. In non controlled airspace the PiC assumes the total responsibility of staying “well clear” (or “see and avoid”) in relation to other traffic. In controlled airspace, the PiC’s responsibility can be partly satisfied by Air Traffic Control (ATC) who takes the responsibility to maintain safe separation between aircraft (at least between those following IFR rules).

The same rules apply to UAS. However, UAS can also operate in some unique ways for which:

• The PiC (possibly supported by observers on the ground) can see his machine and its position in relation to other flying and ground based objects, under Visual Meteorological Conditions (VMC) - so called operations in Visual Line of Sight (VLoS); or

• The RPA is so distant from the crew on the ground, that, even in day VMC, it can no longer be seen - so called Beyond Visual Line of Sight (BVLoS) operations.

In both cases the RPA can fly under VFR or IFR. It is however assumed that IFR/VLoS operations have no practical interest, while VFR/VLoS do not require any D&A on board, since the crew on the ground can “see and avoid”, like it is traditionally required in aviation.

Conversely, VFR/BVLoS operations are considered the most demanding (e.g. need to detect and discriminate the clouds against the background, as well as to identify features on the ground which allow following the desired path), while all RPA capable of flying BVLoS will be equipped with sufficient instrument navigation capabilities (e.g. satellite navigation).

In conclusion, present paper proposes to focus international attention on D&A as necessary to fly in IFR and BVLoS.

V. SCOPE OF DETECT AND AVOID

The pilot on board can see visual aids located at the aerodrome, ground traffic or some meteorological phenomena.

It is proposed that these additional functionalities do not need to drive the design of the D&A, since they could be fulfilled by other means. However, where feasible, the same sensors required for D&A could be used for other functionality e.g. detecting the aerodrome environment and providing synthetic information on the aircraft position in relation to the aerodrome manoeuvring area to the pilot working at his/her Remote Pilot Station (RPS), which is de facto a “cockpit on the ground”.

VI. THE SAFETY OBJECTIVE

Even a small RPA if colliding with a large aeroplane, could cause a catastrophe. Most users of controlled airspace are indeed large aeroplanes, carrying hundreds of passengers for the purposes of Commercial Air Transport (CAT).

Currently civil aviation, in a number of cases, accepts a level of probability for an event (or failure condition) leading to a catastrophe, not higher than the order of 10E-9 per flight hour [5] [6] [7] [8]. This could perhaps be a reasonable starting point to define a safety objective in controlled Airspace Classes A, B, C and D [9]. More substantiation of any figure is however required.

Furthermore, using the ELoS principle and considering that Class G non-controlled airspace is mainly populated by small general aviation aircraft under VRR, one could also look at the historical safety record of general aviation in ICAO Airspace Classes F and G, indeed under VFR: i.e. “see and avoid”. With the limited data available, one could argue that therein the historically achieved level of safety, accepted by the society, also in relation to MAC, is closer to 10E-7 than to 10E-9. Even in this case more consolidated data is however required to substantiate any safety objective.

IFR flight is in any case not prohibited in Class G and some States allow CAT by large aeroplanes even in this airspace Class. IT could be perhaps be assumed that, even in Class G, the risk of MAC between an RPA and a large aeroplane, should not be grater than 10E-9, for each failure condition possibly leading to this.

VII. DETECT AND AVOID IN THE TOTAL SYSTEM APPROACH

Recital (1) of the already mentioned EASA Basic Regulation, mentions the “total system approach”. In this vision, both ATC and D&A, at least in controlled Airspace Classes A, B and C, contribute to preventing close proximity and MAC.

Therein the safety objective could be partially apportioned to ATC, and not totally to the on board D&A.

Furthermore, large aeroplanes, in any airspace Class are equipped with ACAS, which also contributes to achieve the overall safety objective, even in non-controlled airspace.

In order to achieve this, of course the behaviour of the D&A has to be compatible with ACAS. Since RPA flight BVOLS will be, at least initially, be under IFR, it is assumed that the RPA will be equipped with an ATC Transponder. Therefore the RPA will cooperate with the ACAS on board large aeroplanes. Although further study in this compatibility is needed, this issue is expected to be cleared with sufficient safety and without requiring retrofit of current fleet, which would be not acceptable.

In the future however, an enhanced compatibility between ACAS and D&A could perhaps be achieved taking advantage of ADS-B.

In conclusion, not only the safety objectives (e.g. 10E-9 in relation to large aeroplanes and 10E-7 in relation to other aircraft types) need to be substantiated and agreed, but also their apportionment to the various components of the total aviation system: i.e. ATC, ACAS and D&A.


VIII. WHERE THE RULES ON DETECT AND AVOID?In the European Union (EU) the rules for ATM

performance and interoperability are developed through the “single European sky”; the rules for safety through EASA. It seems clear that D&A does not increase the capacity of the EU airspace, while on the contrary it has a very significant relevance in terms of safety. The future rules for it should therefore fit into the structure of current EASA rules.

The applicable and already published EASA airworthiness policy [10] is non exhaustive on D&A, since the operating criteria necessary for the proper functioning of D&A need to be defined in the context of ATM. In fact the purpose of airworthiness is not to prevent mid-air collisions (MAC), but to eliminate/mitigate intrinsic aircraft failures that could lead to a crash.

Both ICAO Circular and EASA policy recognise that D&A is a relevant issue to be solved, before civilian UAS can be allowed to fly in non-segregated airspace. Furthermore, EASA policy contains the principle that a civil UAS must not increase the risk to third parties, compared with manned aircraft of equivalent category: so called principle of Equivalent Level of Safety (ELoS), as discussed above in relation to the identification of possible safety objectives.

In principle EASA rules should cover:

• The obligation to be equipped with a D&A system when flying BVLOS;

• The minimum performance requirements for the D&A equipment.

Two options could exist for the obligation to carry D&A equipment on board: (1) amend the so called “EASA-OPS” [11], or (2) amend the proposed EASA “Part AUR”. The EASA-OPS address only the air operators established in the EU and therefore potentially do not cover the totality of RPA which could populate the airspace under the sovereignty of the EU Member States. For this reason, EASA has proposed “Part AUR” [12], which addresses any aircraft entering the EU airspace. The initial issue of Part AUR is limited to version 7.1 of ACAS software, but amendment is possible, in order to mandate equipment of RPA, intended to fly BVOLS, with D&A.

For the technical features and minimum acceptable performances of the D&A, traditionally the US Federal Aviation Administration (FAA) publishes Technical Standard Orders (TSO). EASA is following the same tradition by publishing European Technical Standard Orders (ETSO). So publishing an FAA TSO, paralleled by an EASA ETSO for D&A could be a possibility. In any case, while the legal tools to impose carriage could be different, since driven by respective legal orders, the technical rules should be harmonised as much as possible across the two side of the Atlantic. This will undoubtedly facilitate the life of industry and contribute not only to aviation safety, but also to free trade in the world.

Finally, the present legal competence of EASA is limited to civilian UAS with a mass of no less than 150 Kg. It seems

however clear that any rules for D&A will be driven by the operational scenario (i.e. IFR/BVLOS) and not by the mass of the RPA.

Therefore, both EASA and FAA are participating to the work of the Joint Authorities for Rulemaking of Unmanned Systems (JARUS), together with other competent aviation authorities from Europe and beyond.

For the technical work, both FAA and EASA are working in cooperation with industry, mainly through RTCA Sub-Committee 204 and Eurocae Working Group 73.

A. Abbreviations and Acronyms 4D Trajectory: definition of the way points to be over flown in terms of three geometrical coordinates, plus time

ACAS: Airborne Collision Avoidance System AMC: Acceptable Means of Compliance AOC: Air Operator Certificate ATC: Air Traffic Control ATM: Air Traffic Management BVLoS: Beyond Visual Line of Sight C2: Command and Control data link CAT: Commercial air Transport CS: Certification Specification D&A: Detect and Avoid EASA: European Aviation Safety Agency ELoS: Equivalent Level of Safety ESARR: EUROCONTROL Safety Regulatory

Requirement ETSO: European Technical Standard Order EU: European Union FAA: Federal Aviation Administration FIS: Flight Information Service FL: Flight Level FMS: Flight Management System ICAO: International Civil Aviation Organisation IFR: Instrument Flight Rules JARUS: Joint Authorities for Rulemaking of Unmanned Systems

MAC: Mid-Air Collision PiC: Pilot-in-Command RA: Resolution Advisory ROC: RPA Operator Certificate RPA: Remotely Piloted Aircraft RPS: Remote Pilot Station TSO: Technical Standard Order UAS: Unmanned Aircraft System UASSG: ICAO UAS Study Group US: United States of America VFR: Visual Flight Rules VLoS: Visual Line of Sight VMC: Visual Meteorological Conditions


REFERENCES

[1] ICAO Unmanned Aircraft Systems (UAS) Circular (Cir 328 AN/190), Montreal, March 2011.

[2] Report of the Techincal Commission on Agenda Item 46 (A37-WP 400), 37th ICAO General Assembly, Montreal, 05 October 2010.

[3] Paragraph 3.2.1 of Annex 2 to the Chicago Convention – Rules of the Air, 10th edition, ICAO Montreal, July 2005.

[4] Regulation (EC) No 216/2008 of 20 February 2008 as amended by Regulation (EC) No 1108/2009 of the European Parliament and of the Council of 21 October 2009.

[5] AMC to EASA certification specification CS 25.1309. [6] EUROCONTROL Safety Regulatory Requirerment (ESARR) 4 “Risk

Assessment and Mitigation in ATM”, 1st edition, Bruxelles, 05 April 2001.

[7] Paragraph 2.1 in ICAO Manual on implementation of 300 m (1000feet) Vertical Separation Minimum between Flight Level 290 and 410 inclusive (Doc 9574 AN/934), second edition, Montreal, 2002.

[8] Paragraph 6.17 in ICAO Manual on airspace planning methodolgy for the determination of separation minima (Doc 9689 AN/953), first edition, Montreal, 1998.

[9] Appendix 4 in Annex 11 to the Chicago Convention – Air Traffic Services, 13th edition, ICAO Montreal, July 2001.

[10] EASA policy statement on airworthiness certification of Unmanned Aircraft Systems (UAS) (E.Y013-01), Cologne, 25 August 2009.

[11] Opinion No 04/2011 of the European Aviation Safety Agency of 1 June 2011 for a Commission Regulation establishing Implementing Rules for Air Operations.

[12] Opinion No 05/2010 of the European Aviation Safety Agency of 18 October 2010 for a Commission Regulation laying down common airspace usage requirements and operating procedures


North Sea Helicopter ADS-B/MLat Pilot Project Findings

Paul Thomas Design Department Bristow Helicopters

Redhill, United Kingdom [email protected]

I. INTRODUCTION

As part of a North Sea ADS-B/Multilateration Pilot Project initiated in 2008, two aircraft were modified to participate in a controlled trial, which culminated in a test and evaluation programme to prove, or otherwise, if ADS-B was viable for rotorcraft engaged in off-shore support and search and rescue operations in the North Sea sector. Two end-to-end flight functional tests were conducted with a Bristow Sikorsky S61N and a CHC Sikorsky S76B helicopter. Both aircraft were equipped with Mode S transponders providing ADS-B extended squitter. This paper summarises the main findings of the test and evaluation programme.

II. SCOPE

The flight functional test plan involved manoeuvres that were within the scope of a normal operational flight regime, to exercise several ‘worst case scenarios’ including GPS constellation aspects [1], proximity to wind turbines [2], combinations of pitch and bank attitudes taking into account antenna placement, all 360° headings and operating altitudes. The purpose was to analyse the operational envelope and identify performance limitations that may necessitate review of the installation requirements.

The flight functional test plan addressed two main components of the ADS-B function:

• GPS satellite signal in space reception and its effects on position determination and integrity, for inclusion in the ADS-B message.

• Reception of the transponder L-band squitter transmissions, containing the ADS-B information.

Reception of L-band transmissions from the aircraft is evidenced by the samples received per antenna of the Multilateration (MLat) network. Each antenna “sees” the transmissions dependent on their relative location to the aircraft. The end-to-end function was verified, with focus on ADS-B since this depends on the aircraft installation and qualification/certification. MLat operates from aircraft L-band transmissions (Mode S replies and ADS broadcasts), but no aircraft installation requirements apply. The test results outline the overall functional operation, including landside sub

functions, so that an overall performance and limitations assessment is obtained. The aircraft ADS-B installations were designed in compliance with applicable airworthiness requirements [3]. The flights departed within 15 min of each other to assure minimum separation and their planned route is shown in Fig. 1.

The flight profile was as follows:

1) de Kooy/den Helder: Slow take off to verify MLat low altitude coverage at den Helder airport

2) Cruise at 1000 ft: No specifics, regular operation 3) Descend to 200 ft: Prepare for aerial work at 200 ft 4) South West of OWEZ Meteorological Mast: A hovering

360° turn at 200 ft (turbine hub height) to exercise all antenna angles relative to GPS reception and L-band transmissions at minimum (0°) bank angle and up to 9° pitch angle

5) West of OWEZ Turbine 12: A right hand 360° turn, ≤15° bank at 200 ft to exercise worst case L-band antenna angles (top/bottom and airframe masking)

6) West of Helder: A figure of 8 normal bank angle at 300 ft to exercise left and right antenna lines of sight at normal pitch and bank angles

7) Platform K14-FB1: A right hand orbit at minimum bank angle and at deck height to verify the effects of the offshore platform on GPS as well as L-band performance

8) Platform K15-FB1: A left hand orbit at minimum bank angle and at deck height to verify the effects of the offshore platform on GPS as well as L-band performance

9) Platform L13-FD1: Avoiding restricted areas EHD-41D, -41A and -41E, descend to sea level, hover for 1 min at 0 kt fwd speed, unspecific heading to verify GPS and L-band performance at sea level altitude (typical vessel survey/rescue operation)

10) KOLAV: Cruise at 1000 ft, no specifics, regular operation, arrival via Mike, Hotel

11) Hotel to EHKD: Approach to land, slow approach to verify MLat low altitude coverage at den Helder airport, regular operation

The flight timings and durations are detailed in Table I.


Figure 1. Route plan overview

TABLE I. FLIGHT DETAILS

Aircraft Times

HDR Departure (Z) HDR Arrival (Z) Total time

S76 09:53:09 11:29:51 5783 s (96.38 min)

S61 10:08:35 12:09:26 7251 s (120.85 min)a

a. S61 flight included a fuel stop

III. GPS ASPECTS

Flight times were selected to coincide with the ‘worst case’ period of GPS satellite visibility to make the test result sensitive to loss of satellite visibility. A fault detection and exclusion (FDE) analysis was performed for den Helder airport assuming a mask angle of 5º. The FDE unavailable period started at around 1230 during early September and lasted for approx 14 min, linearly progressing to start around 1000 in early October and lasting for only 4 min. Therefore, 1000 to 1200 (Z) on 11 October was selected for the flights.

IV. FLIGHT FUNCTIONAL TEST – DETAILED ANALYSIS

For clarity, the most pertinent results for each of the flight functional test phases are detailed for each aircraft type.

A. S76 Departure from den Helder ADS-B was operational from the ground upwards, although

the on-ground returns had interruptions. MLat became operational at 600 ft (6 min later). In general, ADS-B and MLat positions corresponded well with hardly any scatter and the ground track showed the taxi to take off path. Discrepancies between ADS-B and MLat position were in the order of 10 m. Any one antenna would enable ADS-B detection, while an altitude of at least 600 ft was required to enable MLat.

B. S61 Departure from den Helder Fig. 2 details the right hand departure flown at low climb rate to observe the MLat activation point, although the climb rate was faster than the S76. MLat became operational at 600 ft, 1 minute after the first ADS-B return was received. ADS-B and MLat track very well, but with some scatter where MLat starts operation. The sample rates appear to be approximately equal.

C. S76 Turn to OWEZ area Fig. 3 shows how the S76 approached Offshore Wind park

Egmond aan Zee (OWEZ), performing a shallow turn to the right at moderate bank angle. MLat positions were lost, while ADS-B positions show no or few interruptions. There is no evidence of multipath since MLat and ADS-B positions always closely correlate. Analysis of MLat and ADS-B receiver samples show that in 63% of cases the aircraft was seen by only 3 antennas, which is inadequate for MLat position determination. It was deduced that the MLat positions were obtained from samples produced by ≥4 antennas; 22% of the observations were from 4 antennas, 14% from >4. MLat receiver data around OWEZ was analysed and showed the aircraft turned toward the wind turbines, potentially shading its bottom L-band antenna and resulting in two adjacent MLat receivers northwest of the aircraft being periodically masked. Analysis also revealed that the den Helder receiver ant 27(-28) was the dominant receiver. Some very distant receivers picked up ADS-B transmissions, whereas some closer receivers did not.

D. S61 Turn to OWEZ area Fig. 4 shows the S61 descent to 200 ft and right turn

towards the south of OWEZ. During the turn, MLat started to scatter relative to ADS-B positions by approximately 50 m, before being lost altogether for approximately 1.5 km along the 315º track and ADS-B samples were missed, suggesting loss of line of sight with the MLat antennas. Again, den Helder (ant 28) was the dominant receiver and others were used to a much lesser extent. Consequently, it can be assumed that all of these antennas were masked.

Figure 2. S61 den Helder departure – detail

S61 G-BIMU ADS-B/MLat, departure HDR detail

100 m

52.920

52.922

52.924

52.926

52.928

52.930

52.932

52.934

52.936

52.938

52.940

4.780 4.785 4.790 4.795 4.800 4.805

Longitude

Latit

ude

ADS-B positions

den Helder/de Kooy

MLat positions

100 m

10:09:31.39


Figure 3. S76 Turning to OWEZ

E. S76 Hovering turn at OWEZ The hovering turn was performed approximately 350 m away from the OWEZ Meteo mast. Fig. 5 shows ADS-B reports were continuous while MLat was interrupted and scattered in the order of 20 m, probably due to 60% of the samples being received by only 3 antennas, 25% by 4 and the remaining small percentages by 5 or more. Compared to the right turn to OWEZ, it seems that MLat antennas 19(-20) and 27(-28) are periodically masked. ADS-B is continuous, therefore, the aircraft installation has not contributed to masking. MLat scatter is evident throughout the manoeuvre, with evidence of a 10 m systematic error (MLat position south of the ADS-B position). The S76 recorded Horizontal Figure of Merit (HFOM) as between 80 and 150m with HIL down to 600-500 m. The reducing HIL suggests that the GPS constellation (per FDE analysis) was the cause, not manoeuvring or masking. A right turn through heading 60º to 330º was then executed, during which HFOM deteriorated from 80 to 145 m maximum. This isn’t related to heading change or bank angle since, once rolled out on 330º, bank angle should be negligible. MLat coverage was worse than ADS-B, most likely due to the MLat

Figure 4. S61 Turning to OWEZ

Figure 5. S76 hovering turn at OWEZ – detail

receiver masking phenomenon experienced during the hovering turn. Position was lost during westerly and south westerly headings and attributed to aircraft right bank causing transponder antenna masking (aircraft right bank, turning the bottom antenna away from the receivers with the top antenna shaded by the airframe).

F. S61 Hovering turn at OWEZ South west of OWEZ, close to the Meteo mast, a hovering

turn was performed at wind turbine hub height of 300ft (see Fig. 6). MLat and ADS-B were interrupted during the approach from the East. As the hovering right turn commenced, MLat positions return when the heading is 90º (the red arrows show approximate heading). Scatter of 10 m builds up to 30-40 m in some samples, but is not systematic and disappears as soon as the aircraft gained forward speed. The only explanation for ADS-B outlier 50 m to the south is an erratic GPS position sample, either due to multipath or GPS constellation factors. A right turn with forward speed and 15º bank was then performed. Small discrepancies between ADS-B and Mlat are not considered systematic. On the 300º track into the right turn, 1 MLat sample was received for roughly every 4 ADS-B samples. During the turn, the sample rate was approximately equal, but after the turn, the MLat sample rate returned to ¼ of the ADS-B sample rate. Also some ADS-B samples were missing, pointing to compromised visibility of MLat receivers.

Figure 6. S61 hovering turn southwest of OWEZ

S61 G-BIMU, Turn to OWEZ

0 m 500 m

52.570

52.575

52.580

52.585

52.590

52.595

52.600

52.605

52.610

4.400 4.410 4.420 4.430 4.440 4.450 4.460 4.470 4.480 4.490 4.500

Longitude

Latit

ude

ADS-B positions

MLat positions

OWEZ, windpark

0 m

500 m

10:21:42

S76 PH-NZS, at OWEZ - overview

Meteo Mast

52.550

52.570

52.590

52.610

52.630

52.650

4.300 4.350 4.400 4.450 4.500

Longitude

Latit

ude

ADS-B positionsMLat positionsOWEZ, windparkPlatformsden Helder/de KooyMeteo Mast

S76 PH-NZS, hovering turn at OWEZ - detail

Meteo Mast

52.592

52.594

52.596

52.598

52.600

52.602

52.604

52.606

4.375 4.377 4.379 4.381 4.383 4.385 4.387 4.389

Longitude

Latit

ude

ADS-B positionsOWEZ, windparkMeteo MastMLat positions100 m


G. S76 Figure of 8 A ‘Figure of 8’ was performed at 200 ft, normal speed and bank angle. ADS-B and MLat positions were virtually without interruptions or scatter. There was some concurrent MLat and ADS-B loss, possibly due to signal masking, but to a much lesser extent than during the right hand turn. The aircraft used equal but opposite bank angles, so the aircraft installation was not a cause. Some brief MLat interruptions occurred that were uncorrelated to ADS-B. MLat antenna data showed no reason for this. HFOM deteriorated from 72m to 100m, while HIL fluctuated between 500 and 300 m through heading 90º to 200º. HFOM deteriorated again through heading 120º to 330º recovering back to 72m as the bank reduced to zero on heading 330º. The bank angle may have caused masking of one or more GPS satellites. On the S76, the GPS antenna is located on the tail boom with the tail rotor on the left. Thus a right bank would seem to be the worst case. HIL fluctuations were most pronounced during the left turn and also after rolling out on the final heading (330º). Therefore GPS constellation effects are considered more likely than masking.

H. S61 Figure of 8 No discrepancies were noted between ADS-B and MLat.

Both showed good coverage, although a small number of MLat samples were missed during the left hand turn (from 90º-360º deg), while at the same headings in the right hand turn, ADS-B and MLat sample rates were good.

I. S61 Fuel stop at platform Noble Hans Deul The fuel stop at platform Noble Hans Deul provided an

opportunity to view ADS-B and MLat performance during approach, landing and when on deck (See Fig. 7). Generally, coverage was good, with few or no missing ADS-B reports. MLat matches ADS-B closely, although some small systematic discrepancy between ADS-B and MLat position of a few meters may be observed in the hover (red arrows show heading while hovering). MLat positions appear to scatter by a few meters at low speed.

J. S76 Orbit around platform K14-FB1 The orbit was flown in the hover with the aircraft nose

pointing to the platform (see Fig. 8). Some regular differences

Figure 7. S61 approach, landing and departure from Noble – H. Deul

Figure 8. S76, Orbit around platform K14-FB1

between ADS-B and MLat are apparent as well as a systematic discrepancy between GPS and MLat positions. During the orbit, all MLat positions are inside GPS positions by approximately 10-20 m. One explanation may be that MLat locates the transmitting L-band antenna position, while the ADS-B content reflects GPS antenna position (no displacement between the two being applied). On the S76 these antennas are separated by the length of the aircraft, so, the difference should be in the order of 10 m. The same was observed during the hovering turn manoeuvre. As the aircraft establishes forward flight, the difference transforms into an along-track difference, which does not readily show. HFOM deteriorated from 72 to 145 m and HIL from 200 m to 1000 m. At the time, the distance from the platform was ~110 m. K14 is a platform with no tall build up, thus masking by platform structures is not an explanation. Since the orbit was performed in the hover, the bank angle should have been negligible. The HFOM/HIL deterioration was observed between heading 220º to 100º so the only possible explanation (except for the GPS signal in space itself) is masking of the GPS antenna by the airframe.

K. S61 Orbit around platform K14-FB1 Arriving from the North (Noble H. Deul), the left-hand

orbit was flown in the hover with the aircraft nose pointed to the platform, thus exercising all headings at zero bank and minimum pitch (see Fig. 9). Some MLat scatter of approximately 5-10 m was evident, but minimal systematic discrepancy between ADS-B and MLat was observed. Some erratic MLat positions are evident when the aircraft was due north of the platform, but MLat positions were well ‘inside’ ADS-B (i.e. towards the platform). Separation between GPS and L-band antennas on the S61 is approximately half that of the S76 so the antenna displacement effect is less pronounced. In forward flight MLat sample rate is roughly ½ that of the ADS-B sample rate and some ADS-B samples seem to be missing.

L. S76 Orbits around platform K15-FB1 Several orbits were flown around platform K15-FB1, which

S61, G-BIMU, Landing/take-off Noble Hans Deul - detail

53.248

53.249

53.249

53.250

53.250

53.251

53.251

53.252

53.252

3.745 3.750 3.755 3.760 3.765 3.770

Longitude

Latit

ude

ADS-B positions

MLat positions

Platforms

100 m

S76 PH-NZS, LH orbit K14-FB1

100 m

53.186

53.187

53.188

53.189

53.190

53.191

53.192

53.193

3.574 3.576 3.578 3.580 3.582 3.584

Longitude

Latit

ude

ADS-B positions

Platforms

MLat positions

100 m


Figure 9. S61 Orbit around platform K14-FB1

differs from K14-FB1 in that it is very large with tall structures. Fig. 10 again shows a systematic discrepancy between MLat and ADS-B positions of 15 m. As the aircraft transitions to forward flight, this systematic error disappears. HFOM dropped from 55-60 m to 330 m, while HIL dropped from 300 m to >7000 m, particularly during the 4th orbit in forward flight around 80 kts with right bank applied throughout almost all 360º headings. The distance from the platform was around 200 m and HFOM returned to 65 m when rolling out on a heading of approximately 100º. This occurred on most headings at distance from the platform with constant right bank, so GPS antenna shading by the tail rotor is suspected.

M. S61 RH Orbits around platform K15-B1 Fig. 11 shows the orbits performed, first in the hover with

the aircraft nose pointed to the platform as in the K14 orbit, followed by banked orbits at forward speed. In general the results are excellent with no systematic discrepancies, but some MLat scatter is again visible during hovering. ADS-B and MLat sample rates and tracking are satisfactory, although some samples are missing during orbits at forward speed and bank, as indicated by the red dotted line. During turns, MLat samples are ¼ of the ADS-B sample rate, while closer analysis reveals approximately 20% of ADS-B samples were missing.

Figure 10. Orbits around platform K15-FB1

Figure 11. S61 Orbits around platform K15-FB1

N. S76 Sea level operation close to platform L13-FD1 With the aircraft down to approximately sea level to

simulate operations with a survey vessel, position was held for one minute at no specific heading. ADS-B and MLat correlation were generally good. Fig. 12 shows systematic discrepancies between MLat and ADS-B amounting to approximately 10 m. Aircraft heading during the hover changed from 60º to 90º, placing the L-band antenna to the east of the GPS antenna (east is right on the plot), then turning to 120º and transitioning to forward flight (red arrows in the plot). MLat positions show east of the ADS-B positions, confirming the antenna relative placement theory. HFOM and HIL were steady at 60-63 m and 260 m respectively, indicating optimum GPS reception with no signal loss or masking.

O. S61 Sea level operation close to platform L13-FD1 This confirmed earlier observations and showed generally good performance. Discrepancies between MLat and ADS-B are seen in transition to forward flight west north-west of the platform (see Fig. 13). ADS-B reports appear reasonable so MLat scatter is assumed. Some ADS-B reports are missing and there is a 30 m outlier (red dotted line shows the time history).

Figure 12. S76 hovering at sea level

S76 PH-NZS, ADS-B, orbits K15-FB1

100 m

53.272

53.273

53.274

53.275

53.276

53.277

53.278

53.279

53.280

3.860 3.865 3.870 3.875 3.880 3.885

Longitude

Latit

ude

ADS-B positions

Platforms

MLat positions

100 m


Figure 13. S61 Hovering at sea level –sub-detail

P. S76 Transit, return flight and arrival at den Helder ADS-B and MLat positions were aligned during the transit

to den Helder with no scatter and virtually identical sample rates. Increased altitude improved MLat performance. During arrival, ADS-B and MLat corresponded well, but MLat scatter built up to approximately 40 m during descent as MLat reception was lost. The last MLat sample was obtained 1 minute before the final ADS-B sample. MLat position was more scattered during arrival (~40 m) than during departure (10 m). Analysis revealed less MLat receivers were “seeing” the aircraft during the first 2 minutes of departure than during the last 2 min of arrival and different receivers were used in each case.

Q. S61 Transit, return flight and arrival at den Helder ADS-B and MLat positions were perfectly aligned on the transit to den Helder with no sign of scatter and virtually identical sample rates. Again, the increased altitude improved MLat performance. During the final minutes of arrival, MLat visibility reduced to the point where the geometry deteriorates, resulting in scatter as in the case of the S76. MLat continued down to touch down with scatter beginning at 300 ft, 1.5 min before touch down. The den Helder ant 27 is dominant and remains so to the end of the flight. One sample was received by ant 6 on platform D15-FA-1, which at 193 km away is well beyond the radio horizon. It is not clear how this antenna was able to see the aircraft, multipath could be assumed.

V. SUMMARY

The ADS-B and MLat function operated extremely well. ADS-B was available down to sea level with minimal interruption, GPS multipath or masking.

MLat data was scattered at or below 500 ft, but in general, discrepancies between MLat and ADS-B were due to the physical locations of aircraft GPS and L-band antennas.

Only during manoeuvres adjacent to wind turbines was ADS-B lost for an appreciable period. This was attributed to aircraft L-band antenna masking by the wind turbines.

The S76 suffered more L-band masking, but, the S61 suffered more wind turbine masking. This may have been due to the S76 flying lower than turbine hub height.

Longitudinal displacement of upper and lower L-band antennas and their alternate operation mean that MLat positions inherit “scatter”, which is most pronounced in lateral, hovering flight. ADS-B returns are fixed relative to the GPS antenna.

The HIL and HFOM recordings from the S76 revealed unexplained excursions so were attributed to tail rotor masking.

VI. CONCLUSION

The ADS-B installations on both aircraft are completely adequate. Equipment performance is consistent with governing TSOs, while aircraft installation aspects (antenna locations and RF characteristics) perform to expectations.

The flights took place under worst case predicted RAIM conditions so that the minimum number of satellites would be visible, making any antenna masking noticeable.

There is no evidence of GPS position loss. Degraded GPS reception close to two separate platforms was experienced when the S76 recorded significant HIL and HFOM reduction for approximately 60 seconds. This was attributed to satellite masking by the airframe. It has not been possible to positively identify which satellite(s) were masked during those periods.

L-band masking was attributed to the wind turbines, when operating in their close vicinity and at approximate turbine hub height.

Differences between the aircraft antenna installations were expected to cause discernable differences in operational performance, but this was not the case.

Multilateration and ADS-B exhibited small position differences corresponding to the distance between aircraft GPS and L-band antennas. For the subject helicopters these differences are small (less than 10 m).

Compilation of specific helicopter-oriented certification requirements in consultation with the authorities that will satisfy the current and intended service levels for off-shore operations is close to being finalised.

REFERENCES

[1] HPL-VPL analysis for the Northsea area, 11—13 oct 10, INTEGRICOM, P.B. Ober.

[2] Impact of Wind Turbines on WAM, Eurocontrol TRS: 08-112983-E, Roke Manor Research Ltd, Report No: 72/09/R/148/R, May 2009 – Issue 1.

[3] European Aviation Safety Agency Certification Specifications for Large Rotorcraft CS-29 Amendment 2, 17 November 200


LAOTSE, an Approach for Foreign Object Detectionby multimodal netted 2D / 3D Sensors

Session 3.1 page 61

Millimeterwave Radar for Runway Debris DetectionSession 3.2 page 65

OFDMWaveforms for a Fully Polarimetric Weather RadarSession 3.3 page 69

Polarimetry applied to avionic weather radar:improvement on meteorological phenomena detection and classification

Session 3.4 page 73

Principles of Utilization of Polarization Invariant Parameters forClassification and Recognition of Distributed Radar Objects

Part I. Simplest model of a distributed object paper

Session 3.5 page 79

Part II. Multipoint model and correlation theory

Session 3.6 page 83


LAOTSE, an Approach for Foreign Object Detection by multimodal netted 2D / 3D Sensors

S. Hantscher, H. Essen, P. Warok, R. Zimmermann, M. Schröder, R. Sommer, S. Lang

Fraunhofer - FHR 53343 Wachtberg, Germany

[email protected]

M. Schikora, K. Wild, W. Koch Fraunhofer - FKIE

53343 Wachtberg, Germany [email protected]

Abstract— Globalization enforces increasing airplane traffic and thus a growing turnover frequency. This requires a sophisticated and time-efficient searching for sources of danger imposed by debris, executing strong risks for airplane take-off and landings. The project introduced in this paper facilitates and enhances the landing strip observation effectiveness. This fully automatically controlled system enables a faster control avoiding sources of error induced by human observers under all weather conditions. This system assists an increased turnover airplane frequency and makes a cost saving airport management possible. The paper focuses on the techniques used for the runway surveillance, especially the radar part for stand-off object detection of debris and a real-time 2D and 3D time-of-flight (ToF) camera system for close-up surveillance.

Keywords – millimetre wave radar, runway surveillance, time-of-flight camera

I. INTRODUCTION

The horrible crash of the Air France Concorde on July 25th

2000 on Charles de Gaulle Airport in Paris showed the urgent necessity of a system to be able to detect reliably debris on runways before take-off and landing of aircrafts. At this accident, a small metal piece lost by an aircraft, immediately taking off before, hit one wheel of the Concorde and let it explode. Parts of the destroyed wheel smashed into the wing and set it on fire.

Nowadays, as suggested by the International Civil Aviation Organization (ICAO), runways are inspected manually every 6 hours. Due to the increasing traffic density on airports as well as the short time slots between 2 successive starts, a reliable runway surveillance with human aid alone is not possible. That is why the security personnel at airports needs technical decision support. Requirements for such a system are:

• Continuous and covert surveillance • Automatic analysis of the scenario • Alarm in case of anomalies • Independent of weather • Time efficient runway control

Under leadership of Fraunhofer FHR, a contemporary system is under development by a strong alliance of specialized companies and partly funded by the German Federal State of North-rhine-Westphalia. Substantially, the system consists of two parts: a millimeter wave radar for the stand-off surveillance of runways and a real-time 2D and 3D time-of-flight camera system for close-up inspection. The information of both systems is fused to give the optimal support to the safety personnel on the airport. In the following, the subsystems are described in more detail.

II. RADAR SYSTEM

The lower terahertz region is currently under exploration for short range sensors for safety and security applications. This is possible due to an extension of millimeter wave components and devices up to frequencies around 300 GHz [1]. The availability of devices capable to generate a transmit power above 10 mW allows the development of radar sensors in this frequency region. Higher operating frequencies give more sensitivity especially for small target objects, as the relative roughness is bigger. The resolution may be enhanced using a wider bandwidth, which is easier to maintain at higher frequencies, where a wide absolute bandwidth is gained by a narrow relative bandwidth [2]. Imaging radars for security and safety applications allow to extract features to classify or even identify target details of interest.

A. Netted Radar Sensors

The philosophy of the current set-up is based upon a netting of several miniaturized millimeter wave radars, which allow to cover the area of the runways with overlapping segments. To miniaturize the system, the 220 GHz frequency band is used, which allows to reduce the antenna diameter by more than a factor of two in comparison with the more traditional millimetre wave band of 94 GHz [3]. For operation in an airport environment this is essential, as obstacles like the netted sensors along the runway have to be miniaturized in size. At the higher frequency, however, the available output power is less, which result in the necessity to use more sensors to cover the traffic area. On the other hand this geometry has the advantage to see possible threat objects from different


aspect angles, enhancing the possibility for classification and reducing the false alarm ratio.

A demonstrator is developed as a proof of principle, which shows the potential of the envisaged sensor system and is a first step towards a commercialization of that system. The specific millimetre wave hardware is described elsewhere [4] in full detail.

B. System Philosophy

Using the available 220-GHz-radar modules the range of each sensor is limited to about 300 m, which allows a coverage of ½ km of runway. Only more than one sensor can achieve the required coverage. The information content of neighbouring, overlapping and in the end of all radar sensors have to be merged and aligned using overlapping segments of the surveyed area. Fig. 1 shows a sketch of the total sensor set-up.

Figure 1. Demonstrator Set-Up with three Radars

The main tool to detect pieces of debris on the runway is a change detection algorithm, which is applied to thepermanently generated radar images of the surveyed area. Any permanent change is creating an alarm signal.

III. RADAR SENSOR DATA FUSION

The miniaturization of the antennas leads to a netted setup of multiple radars to cover the whole runway. The detection of threat objects is enforced by a change detection algorithm separately for each sensor.

The geometric data fusion between all netted sensors could be done using the position and attitude of each sensor. In this approach the position and attitude must be exactly known, to treat them correctly in a geometric transformation process. To overcome this limitation the overlap regions between the sensors can be used. The relatively high resolution of the sensors allows to take the radar data as input for a classical image based decision process. Adjacent radars produce images with an overlap region. Data from these overlapping regions are used for matching in the image plane. The first step is to detect feature points in each radar image.

Possible feature detectors are the Harris detector [5] or the Scale Invariant Feature (SIFT) detector [6]. These feature points can be matched to another. With these matches an

affine transformation between two images can be computed. This procedure can be easily scaled to an arbitrary number of sensors. The benefit of this approach is the independence of the sensor geometry. In comparison to the first approach only the position and attitude of one sensor is needed to project the detection result on the runway, while the position and attitude of the other sensors may be unknown. The overlap region can be also used to improve the detection results. It may occur, that the radar cross section of a threat object is so small, that it is not detected by the change detection. If this happens in an overlap region, it is probable that it is detected by its adjacent sensor. This would decrease the false alarm rate of the system and increase its probability of detection.

IV. OPTICAL SYSTEM

A. ToF System Concept

Time of flight (ToF) camera systems, as the name suggests, are able to measure the distance between the camera and a reflecting point by measuring the elapsed time. Hence, ToF is – quite similar to a radar – an active ranging system, that needs an illumination source. Basically, a ToF camera emits continuously a light signal with a constant wavelength. Mostly, the near infrared area is used for this purpose, because it is invisible and simultaneously not harmful for the human eye.

Figure 2. PMD CamCube from PMDTechnologies [7] consisting of camera and illumination unit

To measure distances, the light has to be modulatedadequately. Then, it is possible to evaluate it as the modulation of the back-scattered signal differs from that of the emitted one. ToF cameras usually modulate their carrier signal by a harmonic oscillation with the frequency f. Then, the phase difference is given by

c

Rf

22 ⋅=Δ πϕ (1)


whereby R denotes the distance between transmitter and reflecting object and c is the speed of light. Transmit and receive signal are mixed for each individual pixel of the sensor matrix which allows the determination of the phase difference, i.e. by sampling of the obtained correlation function at 4 time samples with the values A1, A2, A3, A4 each shifted by 90° [8]

42

311tanAA

AA

−

−

=Δ−

ϕ (2)

Then, the range is calculated by (1). If the modulation frequency is stable enough, the integration time can be chosen comparably high to improve the signal-to-noise-ratio. However, the choice of the modulation frequency f is crucial as it directly constraints the maximum range. Only phase differences of πϕ 20 <Δ≤ within one period can be used for

range calculation without loss of the unambiguity. Hence, the unambiguity range is given by

TABLE I. TECHNICAL SPECIFICATIONS OF THE PMD CAMERA [3]

Parameter Value

Standard measurement range 0.3 m to 7 m

Repeatability (1σ) 3 mm

Frame rate 40 fps @ 200 x 200 pixels 60 fps @ 176 x 144 pixels 80 fps @ 160 x 120 pixels

Field of view 40° x 40°

Illumination wavelength 870 nm

Power supply 12 V ± 10%

Interface USB 2.0

Operating temperature 0° to 50°C

Storage temperature -20°C to 85°C

Figure 3. Optical image of the test objects (from top to bottom: wooden ruler, spoon, nail file, screw driver, rubber, scissors, push pin, screws)

f

cRunamb 2

= (3)

Commercial ToF cameras often operate with a modulation frequency of 20 MHz, yielding a 7.5 m long measurement area. In order to increase this, signal processing techniques as known from image processing for radar interferometry can be applied to evaluate the phase information of adjacent pixels. Such 2D phase unwrapping methods allow an unambiguous phase determination over multiple periods [9]. Another option would be to use multitone techniques as they are known from automotive applications [10]. An overview about the technical specifications is given in Table 1. The camera lens is changeable and thus customised for different applications, such as for automotive applications, for long rangesurveillance or for medical applications (short range surveillance).

Figure 4. Measurement of empty space (intensity plot)

Figure 5. Result (intensity plot) of various items located at the ground. The data of the empty space had been removed.


Figure 6. 3-D image of scissors shown in the figures above

B. Measurements

The combination of radar technology and optical systems facilitate a reliable surveillance of runways. To demonstrate this, a ToF camera from PMD Technologies GmbH was used to image different objects and materials on a smooth plane (Fig. 3). To demonstrate the potential of such a ToF system, a 870 nm PMD camera comprising of about 200 x 200 pixels was used to image different objects and materials (see Fig. 5).

To obtain detailed and undistorted images a free-space measurement (shown in Fig. 4) has to be carried out with the camera system, which will be subtracted from the following measurements. This is a similar idea as for the surveillance radar which detects objects in the change detection sense. On the one hand, only deviations of the surface of the runway are imaged (change detection), on the other hand, the range increase in dependency of the perspective as well as the intensity distribution of the illumination spot on the ground can

be removed. The result (intensity image) is depicted in Fig. 6. Different objects made of different materials have been imaged. The objects can be identified easily by their shape. This allows a reliable evaluation of the threat on the runways by the airport personnel which can decide, whether a removal is mandatory or not as in the case of a non-dangerous item like a piece of paper. As described in the section above, ToF cameras are able to measure objects three-dimensional. To illustrate this, the scissors from the test set-up in Fig. 3 have been selected exemplarily. Fig. 6 shows the result. The height information is coded by the red colour. The bars as well as blades are depicted in a clear way, such that the item can be identified as scissors. The range deviation could be compensated successfully as the object is imaged on a constant ground height. It should be mentioned that both measurements

can also be carried out under foggy or rainy conditions making the system very attractive for a non-stop operation.

V. CONCLUSION

An innovative solution for runway surveillance and debris reconnaissance was proposed. Radar sensors as well as optical three dimensional sensors were fused to get as muchinformation as possible and to achieve a high and reliable detection rate. The system supports the airport personnel responsible for the safety of aircrafts on runways during take-off and landing by giving an alarm in the case of a dangerous item on the runway. The radar system monitors the runway in change detection mode and gives the fused information to the optical system that images the item in more detail. Test measurements showed the feasibility of this technique by using a ToF camera. Both, the intensity plot as well as the 3D range image enabled a clear identification of the objects.

REFERENCES

[1] A. Tessmann, A. Leuther, M. Kuri, H. Massler, M. Riessle, H. Essen, S. Stanko, R. Sommer, M. Zink, R. Stibal, W. Reinert, M. Schlechtweg, “220 GHz Low-Noise Amplifier Modules for Radiometric Imaging Applications,” Proceedings of the 1st European Microwave Integrated Circuits Conference, pp. 137-140, Sept. 2006.

[2] H. Essen, A. Wahlen, R. Sommer, G. Konrad, M. Schlechtweg, A. Tessmann, “Very high bandwidthmillimetre-wave radar”, Electronics Letters. Vol. 41, No. 22, Oct. 2005, pp. 1247 – 1249

[3] P.D.L. Beasley, G. Binns, R.D. Hodges,R.J. BAdley, “Tarsier®, a millimetre wave radar for airport runway debris detection“, Radar Conference, 2004. EURAD. First European Radar Conference, 2004, Amsterdam, pp. 261 – 264

[4] H. Essen, F. Lorenz, S. Hantscher, P. Warok, R. Zimmermann, M. Schröder, W. Koch, M. Schikora, K. Wild, „Millimeterwave Radar for Runway Debris Detection“; Enhanced Surveillance of Aircraft and Vehicles, Capri, Italien, Sept: 2011

[5] C. Harris and M. Stephens, "A combined corner and edge detector". Proceedings of the 4th Alvey Vision Conference 1998: pages 147-151.

[6] D.G. Lowe, “Distinctive Image Features from Scale-Invariant Keypoints”, International Journal of Computer Vision, 60, 2, pp. 91-110, 2004. „Millimeter Wave Radar for Runway Debris Detection“, to be published, ESAV 2011, Capri, Sep. 2011

[7] www.pmdtec.com

[8] T. Ringbeck, C. Schaller, M. Profittlich, “Kameras für die dritte Dimension,” Optik & Photonik, no. 3, October 2009 [in German language]

[9] T. Ringbeck, B. Hagebeuker, “Dreidimensionale Objekterfassung in Echtzeit,” AVN, no. 7, July 2007 [in German language]

[10] N. Fatihi, S. Hantscher, J. Rubart, C. Krebs, D. Nüßler, H. Essen “Imaging Permittivity Measurements for Automated Material Inspection”, Progress in Electromagnetics Research Symposium, March 2011


Millimeterwave Radar for Runway Debris Detection

H. Essen, F. Lorenz, S. Hantscher, P. Warok, R.

Zimmermann, M. Schröder

Fraunhofer - FHR

D- 53343 Wachtberg, Germany

[email protected]

W. Koch, M. Schikora, G. Luedtke, K. Wild

Fraunhofer - FKIE

D-53343 Wachtberg, Germany

[email protected]

Abstract—For foreign object detection on runways highly

sensitive radar sensors give the opportunity to detect even very

small objects, metallic and non-metallic, also under adverse

weather conditions. As it is desirable for airport applications to

install only small but robust installations along the traffic areas,

millimeter wave radars offer the opportunity of small antenna

apertures and miniaturized system hardware. A 220-GHz radar

was developed, which is capable to serve this application if

several of these are netted to cover the whole traffic area.

Although under fortunate conditions the radar allows a

classification or even an identification of the debris, the complete

system design incorporates 3-D PMD-cameras for assistance in

the identification process, if the environmental conditions allow

for this. The latter part of the system is described in a separate

paper [3].

Keywords- FOD; mmW-radar; high resolution; netted sensors

I. INTRODUCTION

The detection and classification of runway debris is a major

concern for airport operation, as related flight delays and

accident claims as well as aircraft repairs are very costly. In

forthcoming years traffic on airports will even increase and

thus the demand on maintaining an adequate safety level. For

safe airport operation it is necessary, that the runways and

taxiways are permanently in an undisturbed condition.

Following the recommendations of the ICAO, the

inspection of runways is done at a period of 6 hours, visually

by security personnel at the time being. This procedure is

costly and may be subject to human failure and is also highly

dependent on weather and daylight conditions. Especially at

adverse weather the visual inspection cannot guarantee the

detection of small metallic particles like screws. Moreover the

traffic density on airports has been considerably increasing,

and a secure inspection between consecutive take-offs and

landings by human visual inspection is not possible.

A few automatic or semi-automatic systems have been

proposed or are in use at some airports to serve this purpose

[1]. Video surveillance is one of the proposals, but suffers

from obscuration by bad weather. Radar sensors, which are

capable to be operated under all weather conditions [2] are

also taken under consideration.

The radar proposed in this contribution is operating at

millimeterwaves with high bandwidth to achieve a very good

range resolution. The 220 GHz band is used, which in

comparison to lower frequencies allows considerably smaller

antennas with high directivity and thus a miniaturization of the

complete set-up. Further a wider bandwidth and thus a higher

ranger resolution can be accommodated at higher frequencies.

As output power at this frequency band is limited, a netted

approach, using several radars along the traffic area, is

proposed.

II. 220-GHZ “LAOTSE” RADAR CONCEPT

A. System ConceptThe LAOTSE [3] concept is based upon a net of

miniaturized 220-GHz radar modules, which are positioned

along the traffic areas. This netted approach is necessary as the

output power of each radar is not sufficient to cover the whole

traffic area, and additionally the probability of false alarms is

considerably reduced for areas, which are monitored from

different aspects. The total LAOTSE system is additionally

using distributed PMD cameras, which are focussed to objects,

which have been detected by the radar [4] and which are also

part of the sensor net.

B. Radar GeometryTests of an experimental LAOTSE system, with a limited

number of sensors are conducted on the Cologne-Bonn

“Konrad-Adenauer” Airport.

Figure 1. Photo of Cologne-Bonn Airport with LAOTSE Site, old Runway

(below) and Cross-Wind Runway

The first LAOTSE radar module is mounted at a slightly

elevated position near to the crossing of the old runway and

the cross-wind runway to be able to overlook a certain area.

The project is financed by the Ministry for Innovation, Science and Tech-

nology of the German Federal State of North-Rhine Westphalia under the

ZIEL2 Programme of the European Fund for Regional Development (EFRE)

LAOTSE

Test site


Fig. 1 shows the situation, Fig. 2 demonstrates the geometry.

Fig. 3 shows the illumination by the antenna beam.

Figure 2. Typical Geometry for Radar above Runway

Figure 3. Illumination of Traffic Area by one Radar Module

To miniaturize the system, the 220 GHz frequency band is

used, which allows to reduce the antenna diameter by more

than a factor of two. This is essential for operation in an

airport environment, where obstacles like the netted sensors

along the runway have to be avoided. This approach, however,

demands for a wider net of sensors to cover the complete

runway area. For complete demonstration of LAOTSE

performance a second sensor will be installed along the old

runway to maintain an overlapping survey region.

In the following, the approach for a single radar module is

described.

C. 220-GHz Sensor Front-EndThe system concept is based on a design, which was

already realized at 94 GHz [5]. The radar FM-CW front-end

uses miniaturized, monolithically integrated components in

GaAs technique [6]. To be capable to cover a bigger range,

like 200 m, coherency of the signal is most essential.

The classical FM-CW approach uses a reference signal,

which is derived from a voltage controlled oscillator (VCO),

which generally is not stable enough to maintain the necessary

coherency over a range > 200 m. This is due to the fact, that

for tuning of the oscillator the Q-factor of the resonator must

not be too high, which may lead to an uncontrolled frequency

drift. The drift can be so high, that during the transmit-receive

cycle the phase of the transmit signal has been changing so

much, that the mixing between reference- and receive- signal

does not result in a sharp frequency response but to a widening

of the receive signal. This widening increases with range and

at a certain range limit no resolution can be achieved. As this

concept does not allow, to achieve a sufficient range, a

concept as sketched in the block diagram of Fig. 3 was

developed. The output frequency is shifted to 105 GHz instead

of 94 GHz and a subsequent doubling results in an operating

frequency around 210 GHz.

Figure 4. Block Diagram of 220-GHz Front-End

All system frequencies are generated or derived from a

stable crystal oscillator. A dielectric, resonator stabilized

oscillator (DRO) generates the reference frequency of 17.2

GHz upon which the radar waveform, a linear FM-chirp with a

bandwidth of 220 MHz – 380 MHz is mixed. A multiplication

by a factor of 6 leads to a basic millimetre wave frequency

band of 104.52 GHz – 105.48 GHz. After amplification this

signal is split into the transmit branch and the local oscillator

branch. The latter is used for downconversion of the receive

signal using a subharmonic mixer. The transmit signal is

derived by doubling to result in a frequency band of 209.04

GHz – 210.96 GHz, a total bandwidth of 1.92 GHz, equivalent

to a range resolution of about 8 cm.

The RF front-end is quite compact and avoids long

waveguide connections. Fig. 5 shows a photo.

Figure 5. Photo of LAOTSE 220-GHz Front-End

The complete radar RF part is mounted within the primary

focus of the Antenna. IF and DC connections to the data

RADAR


acquisition and control box are maintained by rotary joints

leading through the centre of the antenna pedestal.

For the first experiments a self contained steering and data

acquision box was developed, which is capable to operate one

single LAOTSE sensor, to store the measured radar and

distribute all relevant data into the sensor network.

For the operational system only a miniaturized data-

interface box will be assigned to each radar sensor. An FPGA

based processing electronics will deduce high resolution range

profiles by applying an FFT algorithm , which contain the

information on RCS and position of debris objects, and which

serve as a basis for further netted processing and the change

detection algorithm.

D. Antenna and PedestalAs the FM-CW principle is used for the LAOTSE radar,

special care has to be taken for a good isolation between receiver and transmitter, which is essential for a good dynamic range of the system. For an optimum performance, separated antennas were used. To allow a wide operation bandwidth, which is necessary for a high range resolution, offset feed antennas were used. To avoid long waveguides serving the two

antenna feeds, a design as shown in Fig. 6 was used.

Figure 6. Sketch of Antenna Assembly and Photo of Radar upon Pedestal

Each radar module is mounted upon an antenna pedestal capable to survey 360° angular region in azimuth and + 20° in elevation. During operation the elevation is fixed according to the geometrical conditions. Fig. 6 shows also a photo of a radar

module on its pedestal.

III. TEST MEASUREMENTS

A. ISAR Measurements on small ObjectsTo test the sensitivity of the system and to evaluate the

range under which small items can be detected, measurements with an assembly of 25 different small objects on a turntable at a range of about 200 m were conducted. The objects were turned over 360°, and the measured data were evaluated using an Inverse Synthetic Aperture (ISAR) algorithm, which, in contrary to the final LAOTSE algorithm gives a synthetic radar

image of each object. Tab. 1 gives a catalogue of the measured

objects.

TAB. 1

CATALOGUE OF MEASURED ITEMS

Objects

Tools Metal Pieces dielectric Pieces

pliers Wire bundle plastic 100mm x

40mm x35 mm

screw driver screw M8x60 plastic tray 2 shelf

folding ruler metal bar 30mm

x30mm x 30mm

plastic tray 3 shelf

file wheel d = 75mm wood pieces

100mm x 40mm x

20mm

saw antenna fixing stone

paint brush metal plate 200

mm x 15 mm

cable channel

(plastic)

Fig. 7 shows a photo of the assembly of parts on the

turntable.

Figure 7. Photo of Sample Pieces on Turntable

Before the measurements, the system was calibrated by

means of a trihedral corner reflector, which was positioned in

the middle of the turntable. This allows a thorough phase

calibration, which is necessary for the ISAR process and at the

end allows to assign a distinct radar cross section to each item

on the turntable.

Figure 8. Accumulated ISAR Image for 360° Aspect Angle Range

The evaluation was done using different radar bandwidths

ranging from 2 GHz to 8 GHz, which corresponds to a


resolution of about 2 cm to 8 cm. Fig. 8 shows an ISAR image

accumulated over the full 360° aspect angle range, which

means, that each objects was measured from all sides, and

which is more related to the approach of netted sensors, which

also allow to see an object from considerably different aspect

angles.

More realistic for the result for a single scanning radar

sensor is an evaluation, where only a limited fraction of aspect

angles is evaluated. Fig. 9 shows an image related to that of

Fig. 8 with an evaluation aspect range of only 10°.

Figure 9. ISAR Image for 10° Aspect Angle Range

The evaluation showed, that all small items could be

detected and imaged independent of aspect angle. Only for dry

wooden blocks this did not apply, as they could be seen only

at those angles, where they were facing the radar. If they were

only a little bit humid, they were also visible for all aspect

angles. Resolution does not play an important role, the

detectability is not influenced for a resolution range between 2

and 8 cm. It can be concluded, that for an operational system a

resolution of about 10 cm is fully sufficient.

B. Test Measurements with the LAOTSE RadarWhile for the test measurements, described above, the radar

was fixed and the objects were turning to be visible for all

aspect angles, further tests were conducted with the

operational geometry of a scanning radar. By chance the

weather was very bad with low visibility during the

measurements. Fig. 10 shows a photo of the measurement

scene. Fig. 11 shows the result of a sector scan. Clearly the

fence surrounding the terrain can be identified by its very

strong radar echo (red) and the returns of some small items in

front of the fence.

Figure 10. Photo of Test Scenario

Figure 11. Radar Image for Sector Scan over Scene shown in Figure 7

IV. CONCLUSION

Radar modules at a frequency of 220 GHz were developed as elements for a netted set-up along traffic ways on airports. It was demonstrated, that also under adverse weather conditions small objects, metallic and non-metallic could be detected. Further research is done on sensor data fusion among several radar modules and additional 3-D IR cameras [4] using photonic mixer devices technology. This topic is highlighted in

a separate paper.

REFERENCES

[1] P.D.L. Beasley, G. Binns, R.D. Hodges,R.J. BAdley, “Tarsier®, a millimetre wave radar for airport runway debris detection“, Radar

Conference, 2004. EURAD. First European Radar Conference, 2004, Amsterdam, pp. 261 - 264

[2] I. J. Patterson, “Foreign Object Debris (FOD) Research”, International Airport Revue, Issue 2, 2008

[3] H. Essen, G. Luedtke, P. Warok, W. Koch, M. Schikora, K. Wild,”

Millimeterwave Radar Network for Foreign Object Detection”, 2nd Intl. Workshop on Cognitive Information Processing (CIP), Elba, June 14 –

16, 2010

[4] T. Ringbeck, “A 3D Time of Flight Camera for Object Detection”, Optical 3-D Measurement Techniques 09-12. 07. 2007, ETH Zurich

[5] H. Essen, A. Wahlen, R. Sommer, G. Konrad, M. Schlechtweg, A.

Tessmann, “Very high bandwidthmillimetre-wave radar”, Electronics Letters. Vol. 41, No. 22, Oct. 2005, pp. 1247 – 1249

[6] A. Tessmann et al. “220 GHz Low-Noise Amplifier Modules for

Radiometric Imaging Applications,” Proceedings of the 1st European Microwave Integrated Circuits Conference, pp. 137-140, Sept. 2006.


OFDM Waveforms for a Fully Polarimetric Weather Radar

O.A. Krasnov, Z. Wang, R.F. Tigrek, P. van Genderen Delft University of Technology

International Research Centre for Telecommunications and Radar (IRCTR)/ MTSRadar Delft, The Netherlands

Abstract—Retrieval of cloud parameters in weather radar benefits from polarimetric measurements. Most polarimetric radars measure the full back scatter matrix (BSM) using a few alternating polarized sounding signals. Using specially encoded orthogonal OFDM signals however, the BSM can be measured in a single simultaneous transmission of two orthogonally polarized signals. Based on a set of parameters for weather radar, the properties of such a signal are explored and its merit as a useful capability is shown.

Keywords; weather radar; polarimetry; OFDM; coded waveforms.

I. INTRODUCTION

Weather radar is an important source of information for air traffic control and, in general, for meteorological services and research of the environment. For retrieval of parameters on the water content of clouds, it is important the measure the full backscatter matrix (BSM) of clouds, with a highly detailed analysis of the Doppler shifts. Currently, various radars are capable of measuring the BSM, but the waveforms used consist of transmitting sounding signals in two orthogonal basis directions alternatingly. So in order to measure the full BSM two consecutive transmissions are needed. By consequence, some time has passed in between the two transmissions and due to the decorrelation of the echoes of the hydrometeors the quality of the parameter retrieval is somewhat compromised. The research addressed in this paper explores orthogonal signals, such that the sounding signals in the two orthogonal basis directions can be transmitted at the same time, so that the BSM can be measured using a single – though complicated - transmission. Also in [1] the measurement of the BSM using orthogonal signals has been addressed.

IRCTR has developed a radar platform called PARSAX (Polarimetric Agile Radar in S- and X-band; currently the radar is being set to work in S-band) supporting to do such analyses with a variety of waveforms [2]. In [3] and [4] the problem of simultaneous transmission of orthogonal signals has been addressed using various modes of Frequency Modulated Continuous Wave (FMCW) modulation. The current paper explores a particular pulse coded waveform, Orthogonal Frequency Division Multiplexing (OFDM). It will be demonstrated that such waveforms can be used, albeit with a number of constraints.

The paper is structured in the following way. Chapter 2 will detail the OFDM sounding signal both in a generic way and in the specific coding that is used for the benefit of the isolation of the two orthogonal components. Chapter 3 discusses a set of parameters applicable to this type of weather radar. Chapter 4 addresses the effects of Doppler shift. Doppler shift is a parameter that is of vital relevance for the retrieval of the cloud parameters. However, in OFDM is the major cause of loss of orthogonality. Chapter 5 concludes the paper.

II. SIGNAL DESCRIPTION

OFDM is a waveform that is widely used in communication [5]. It is being considered for application in radar only recently [6], inspired by the availability of signal generators due to the application in communication. The signal is composed of a number of carriers at a mutual, constant spacing that is the inverse of the signals’ duration. The complex baseband description is:

( ) ( )

( )

1

02 0

0

N

kk

s t a exp j tk / f t T

s t

−

=

= − ≤ <

= , elsewhere(1)

where N is the number of carriers, ka is the complex

amplitude of carrier k (also called the code of the carrier) and f is the frequency spacing between the carriers. T is the

duration of the signal, 1T f= .

All carriers composing this signal are mutual orthogonal, as can also be seen from Fig.1 representing a detail in the power spectral density of an OFDM signal.

Usually the duration T of the signal is quite long and therefore some type of pulse compression is applied in order to achieve an appropriate range resolution. Given the nature of the OFDM signal, the most widely known procedure is to transform the received signal to the frequency domain and multiply the carriers with the complex conjugate of the Fourier transformed transmitted signal. Then after this multiplication the time domain range profile is generated by the inverse Fourier transform. The level of the sidelobes in this range profile can be managed by any weighting technique.


The carrier codes ka can be chosen independent from each

other. They have an important impact on the signal envelope in the time domain to the extent that the ratio between the peaks and the average of the power is affected. Certainly, the envelope is not constant and the transmitter cannot be driven into saturation as it usual in radar, reason for radar engineers to be reluctant to apply this type of waveform. Special coding optimizes the peak to average power ratio [6, 7].

One of the comments on the Doppler sensitivity of the OFDM signal is that as soon as the Doppler shift is no longer very small compared to the carrier frequency spacing, the sidelobes of the compressed signal come up rapidly and the orthogonality is lost. When the carrier codes are random variables, the sidelobes will ultimately be proportional to 1 N .When on the contrary, the codes are chosen to be equal, the loss of the sidelobes will be negligible, but the peak to average power ratio will be equal to N , so the signal envelope will be very spiky.

The concept of the fully polarimetric radar is based on an approach as shown in the block schematic diagram of Fig. 2. It is shown there that one of the two signals is exciting one of the two polarization directions, while the other signal is exciting the orthogonal polarization direction. Then on receive, the two signals are separated in the pulse compression process by using the two complex conjugated transmitted waveforms as a reference. If no special precautions are taken, the cross compressed signal, i.e. the signal after application of the code of the other signal for the purpose of the compression, will be noise-like. The resulting average signal power is called here the isolation of the codes. An important case for achieving high code isolation can be appreciated by comparing Figs 1a and 1b.

a. two orthogonal sets of carriers, all having a different random code

b. two orthogonal sets of carriers, one with odd, the other with even

carriers. All carriers have a different, random code.

Figure 1. Power spectral density of OFDM waveforms. Subplot a shows a regular set of OFDM carriers. Subplot b shows a set of two special cases, having either only the odd frequencies or the even frequencies. Their code

isolation when the Dopplershift is zero is perfect.

The underlying assumptions are that in Fig1a the two orthogonal signals show a full spectral occupancy, i.e. all values of ka are non-zero, and additionally by assuming that they are all have the same amplitude but a random phase in [ )0 2, . In Fig.1b the sounding signals are different to the extent that transmitter 1 is using all carriers with even values of k and transmitter 2 is using all carriers with odd values of k .The output of the “compression” process of a signal received in one of the two channels using the code of the other channel consists of the code isolation. It will be illustrated later in this paper that it is proportional to 1 N in the first case (two fully populated sets of carriers) and close to perfect isolation in the second case (two disjoint sets of even and odd carriers respectively).

According to this line of thinking, the signal used for the experimentation for the fully polarimetric radar is defined as

( ) ( )

( ) ( )( )( )

2 1

20

2 1

2 10

2 2 0

2 2 1 0

0

N /

H kk

N /

V kk

s t a exp j t k / f t T

s t a exp j t k / f t T

s t

−

=−

+=

= − ≤ <

= − + ≤ <

= elsewhere

,

(2)

Here ( )Vs t represents the sounding signal exciting the

vertical-polarization and ( )Hs t the horizontal polarization.

Figure 2. Simplified block schematic diagram of the radar. (the ⊗ symbols refers to coding/decoding)

OFDM1 OFDM2

R

H

H

V

V

HV

sVH

sHH

sVV

sHV

Transmitter

Receiver


An important side effect of this coding however is that the unambiguous range is half the value of the full set of carriers. Also, the method for reducing the peak to average power ratio fails in this coding.

III. A SET OF PARAMETERS FOR WEATHER RADAR

In the application of weather radar as described in the introduction, a high resolution of the speed is needed, a typical value being 0 1rv . m / s ec= . For an X-band radar, this corresponds to a Doppler resolution of 6.6Hz and thus on a time interval of evaluation of the phase of 0.15sec. In order to appreciate the significance of the Doppler resolution, let us develop a benchmark set of radar parameters compliant with this requirement.

For the application of detailed weather measurements using the X-band radar, the maximum range should be not less than 10km. Since in OFDM the maximum range corresponds to

2u ,maxR c f= , and given the effect of the odd/even coding, this

translates into a frequency spacing 37 5 10f . Hz< ⋅ . Because the duration of this carrier spacing is directly linked to the time duration of the signal by 41 1 33 10T f . s−= = ⋅ , also the pulse repetition frequency is an immediate consequence:

37 5 10PRF . Hz= ⋅ . Hence, using a Doppler filterbank, the number of pulses to be integrated in order to arrive at the required Doppler resolution is 30 15 7 5 10 1125pN . .= ⋅ ⋅ = . The number of carriers affects two parameters at the same time: the bandwidth, fB N f= ⋅ , and the sidelobes of the pulse compression in case of too high a Doppler shift, so in case of loss of orthogonality, SLL=1/Nf. The range resolution of weather radar is a compromise between sensitivity (given the number of rain droplets per resolution volume) and detail in finding the structure of clouds. A fair number is to assume that it should not be lower than 3m, hence

( )2 50B c R MHz= ⋅ = and the corresponding number of carriers is Nf=6667. The correlation noise floor, which is the mean value of the sidelobes of the pulse compression in the case of total loss of orthogonality, then is 38SLL dB≈ − w.r.t. the peak value in case of full orthogonality.

IV. THE EFFECTS OF DOPPLER SHIFT

The concept of orthogonality of the various carriers in the OFDM waveform is compromised if the carriers are shifted due to Doppler. In communications this effect is called Inter Carrier Interference (ICI). Effectively the impact of Doppler

a. Correlation noise b. Code Leakage Figure 3. Comparison of the correlation noise and the code leakage of the two

full sets of carriers (solid lines) and the odd/even carriers (dashed lines).

shift depends on the ratio of the Doppler shift to the frequency spacing between the carriers. In case of randomly coded carriers, the pulse compression in the receiver will fully fail as soon as the Doppler shift equals the carrier spacing.

Because of the same reason of loss of orthogonality, also the code isolation is affected by a Doppler shift and it is getting more reduced the higher the ratio between Doppler shift and carrier spacing is.

In the case of fully random carrier coding, the correlation noise floor is described by [8]:

( )

( ) ( )

( )

1 1 1 12

20 0 0 0

21

2

f f f fN N N Nf

k l m nf

f

m k n lexp j

N

N n lexp j

N

− − − −

= = = =

− −⋅

=−

−(3)

Here df f= is the ratio between the Doppler shift and the carrier spacing. This expression is valid for the fully occupied set of carriers. A graphical representation is in Fig.3a, while in Fig.3b the code leakage is presented as a function of the relative Doppler shift . In these graphs also the corresponding graphs are shows for the odd/even carriers waveform.

These graphs demonstrate two major effects, the correlation noise and the code leakage.

A. Effects on the correlation noise floor The correlation noise, consisting of the range sidelobes in

the co-polar channel due to the Doppler shift, is very low for low Doppler shift. More precisely, if the Doppler shift would be exactly zero, the profile of the range sidelobes would correspond to the weighting function used in the pulse compression. Just off the zero Doppler values, the waveform with all carriers used has a slightly lower correlation noise floor than the odd/even carriers waveform, as a consequence of the higher number of carriers. In all other cases, composing the greater part of the span of Doppler values, the odd/even waveform is superior.

B. The code leakage Regarding the code leakage, consisting of the signal having

code 1 leaking into the other channel and there compressed using the code 2, the odd/even waveform is superior to the waveform with all carriers used for the Doppler shifts of interest. Obviously, this was the reason why this waveform was devised. Such a property is extremely important. It supports to effectively filter away the strong echoes from stationary ground objects, that otherwise might clutter the meteorological echoes. It should be reminded though that at system level the total leaked power from one channel into the other channel is not just due to code leakage, but also due to other system components, e.g. the antenna. A typical number for co/cross polar antenna isolation is 25-30 dB for parabolic antennas. Calling AI the antenna co/cross polar isolation and CI the


code isolation, preferably the inequality max p A C tS G I I N+ − − < should hold (all values in dB). Here

maxS is the maximum signal that is within the linear dynamic range of the receiver,

pG is the gain due to the processing, and

tN is the level of the thermal noise. Using for instance 70maxS dB= with respect to thermal noise,

( )10 1125 30 5pG log . dB= = , 30AI dB= then it follows

70 5CI . dB< . Such a value is achieved for Doppler shifts close to zero, but not for 0 02 150 2 25df . f Hz . m / sec> ⋅ = ∝ . Thus stationary discrete objects, also if some internal motion is present, don’t pose a problem. Hydrometeor echoes themselves will be much weaker than the discretes and don’t pose a problem either.

V. DISCUSSION AND CONCLUSION

Using OFDM-coded waveforms tailored to minimize leakage of signals from one channel out of two orthogonally polarized channels into the other one is considered to be very promising. Such coding outperforms any other type of coding identified so far on the figure of merit for isolation of the codes. Apart from the straightforward coding as shown in this paper, also other codes can be devised, always based on the orthogonality of the carriers in the waveform. A recent experimental result obtained using the PARSAX radar for the simultaneous transmission of an odd/even coded pair of OFDM-signals as in (2) is shown in Fig.4. It shows the range profiles of each of the four elements of the BSM for a single OFDM-sweep. The number of carriers is N=25000 and the total bandwidth is B=50MHz. The carriers are random QPSK-coded. The antenna is pointing at the horizon while the weather was clear. The reflections in this example therefore only concern ground objects. The vertical scale is adjusted to have the highest value (actually the object concerned is a tall chimney) at 0dB. The floor of the signal level is close to the dynamic range of the receiver, approx. 70dB. More details are in [8].

The limit of applicability of the coding comes from the loss of orthogonality, as it can be caused for instance by the Doppler effect. Waveforms like Linear Frequency Modulation are far more tolerant to Doppler, but cannot provide the isolation between the codes as discussed here unless the orthogonal signals are timed in a partially overlapping order, like proposed in [4].

An effect that has not been addressed in this paper, but that is highly important for keeping the carriers orthogonal while receiving echoes with an unknown delay, is the necessity to introduce a cyclic prefix as an extension to the duration T of a single pulse. This effect has been discussed in detail in [9]. It doesn’t invalidate the conclusions.

0 5 10 15

-80

-60

-40

-20

0

HH

range, KM

ampl

itude

, dB

0 5 10 15

-80

-60

-40

-20

0

VH

range, K

ampl

itude

, dB

0 5 10 15

-80

-60

-40

-20

0

HV

range, KM

ampl

itude

, dB

0 5 10 15

-80

-60

-40

-20

0

VV

range, KM

ampl

itude

, dB

Figure 4. Rangeprofiles of the four elements of the BSM. The polarization basis is linear, V/H

In this paper the orthogonality of codes was exploited for the benefit of measurement of the BSM in fully polarimetric radar. The concept of orthogonality might also be used in different applications, for instance for the benefit of retrieving the echoes from multiple beams generated by phased array antennas.

REFERENCES

[1] D. Giuli, M. Fossi, L. Facheris, “Radar target scattering matrix measurement through orthogonal signals”, Vol.F of the Proc. of IEE, Vol.140, issue 4, 1993, pp233-242

[2] O.A. Krasnov, L.P. Ligthart, Z. Li, P. Lys, F. van der Zwan, “The PARSAX — Full Polarimetric FMCW Radar with Dual-Orthogonal Signals”, Proc. EuRAD 2008, Oct. 2008, Amsterdam, The Netherlands, pp. 84–87.

[3] G. Babur, Processing of Dual-Orthogonal CW Polarimetric RadarSignals, PhD thesis, TU Delft, 2009.

[4] G. P. Babur, O.A. Krasnov, L. P. Ligthart, “Quasi-Simultaneous Measurements of Scattering Matrix Elements in Polarimetric Radar with Continuous Waveforms Providing High-Level Isolation in Radar Channels” Proc. EuRAD2006, 30 Sep-2 Oct 2009, Rome, Italy, pp 1-4

[5] R. Prasad, OFDM for Wireless Communication, Artech House, 2004 [6] N. Levanon, Radar Signals, Hoboken, NJ, John Wiley&Sons, 2004 [7] M.R. Schroeder, “Synthesis of Low-peak-factor Signals and Binary

Sequences with Low Autocorrelation”, IEEE Tr. Information Theory, Vol.16, No.1, January 1970, pp 85-89

[8] Z. Wang, R.F. Tigrek, O.A. Krasnov, F. van der Zwan, P. van Genderen, A.Yarovoy, “Application of I-OFDM Signals for Simultaneous Polarimetric Measurement”, Proc. EuRAD2011, 12-14 Oct 2011, Manchester, UK

[9] R.F. Tigrek, A processing Technique for OFDM-Modulated Wideband Radar Signals, PhD thesis, TUDelft, 2010


Polarimetry applied to avionic weather radar:improvement on meteorological phenomena detection

and classification

Alberto Lupidi1, Christian Moscardini2, AndreaGarzelli3, Fabrizio Berizzi4, Fabrizio Cuccoli5

CNIT-RaSS (National Interuniversitary Consortium forTelecommunication-national laboratory of radar and

Surveillance Systems)Italy

{1a.lupidi,2c.moscardini,4f.berizzi}@ iet.unipi.it3andrea.garzelli @ dii.unisi.it

5fabrizio.cuccoli @ cnit.it

Marcello Bernabò

SELEXGalileo S.p.A.Campi Bisenzio-Italy

marcello.bernabo @ selexgalileo.com

Abstract—Avionic Weather Radar is an essential equipment inaircraft. Polarimetry can improve the detection and theclassification of hydrometeors and thus the safety and theefficiency of the flight. Here a 3D polarimetric radar simulatorfor the feasibility study on avionic weather polarimetric radar ispresented.

I. INTRODUCTION

In current avionic systems is impossible to distinguish the typeof precipitation, water, snow, hail. Of course, assumptions canbe done, i.e., high reflectivity in a zone where temperature is15-20 degrees below zero is likely to indicate an hailstorm, butwe can have no precise information on type of precipitationnear and below the melting height (which also depend onseason and geographic region). About 70% of the high-reflectivity echoes that pilots see on their radar is non-hazardous (other than causing a decrease in visibility andmaking runways wet). To determine whether or not a particu-lar “red” echo is hazardous in terms of turbulence and hail andother dangers, the pilot must first know if the atmosphere inwhich he is flying is conducive to of hail and high turbulence.It is worth noting to recall that heavy rain without turbulence isnot an issue for the safety of the flight. But even withatmospheric knowledge, a pilot cannot say whether a particularhigh-reflectivity area is hazardous. Usually, the pilot evadesthat area, with an increase of costs, time and pollutingemissions due to the detour. The use of polarimetry can helpgiving us more precise details on hydrometeor types [1].

For example, rain tends to have an elliptical form withminor axis oriented vertically, resulting in HH signal to behigher than VV signal thus having a positive high DifferentialReflectivity. On the contrary, hail, due to its tumbling motion,appears as spherical, thus having a nearly zero DifferentialReflectivity, even at higher reflectivity (and higher hazard)level. Classification algorithms which utilize the polarimetricinformation on the three channels (HH, VV, HV/VH) can be

developed with the knowledge of Total (Z) and DifferentialReflectivity (ZDR) and Linear Depolarization Ratio LDRdefined as

DR HH dBZ VV dBZZ Z Z (1)

VH dBZ HH dBZLDR Z Z (2)

In this work we assume X-Band based system (around 9-10 GHz) that are preferred because they have an antennawhose dimensions are compact and compatible also forbusiness aircrafts. Polarimetric classification algorithms forground based S-C bands systems already exist and in generalthere is no great difference between ground based and airborneoperation in the application of these algorithms [2], [3].Differences arise from the technical limitations of the airbornesystem, like antenna size, transmitted power and scanningspeed. Main issues for avionic weather radars in conjunctionwith the use of X-band are:

1. heavy beam path attenuation and Mie scattering effects

2. ground clutter

3. wider beam width

4. data availability

In this paper we did not deal with path attenuation and groundclutter. These problems will be addressed in future works. Wesolved the problem of data availability simulating real radardata with a physical based approach described later. Section IIdescribes the scenario and the mentioned approach, while insection III and IV we show some results and conclusionrespectively.


II. ATMOSPHERIC SCENARIO AND RADAR MODEL

One of the problems in weather radar engineering is theavailability of data. Moreover, in radar meteorology, data areavailable mainly in S-band (around 3 GHz) because this is theband chosen for ground based weather radar. To simulaterealistic polarimetric radar data in X-Band, the two mostimportant things we need to know to compute the radarreflectivity are:

1. the Drop Diameter Distribution (DSD) of hydrometeorsN(D) measured in m-4

2. their polarimetric Radar Cross Section (RCS) H,V Dmeasured in m2.

Polarimetric reflectivity is finally computed as

4

, ,50

( ) ( )0.93H V H VZ D N D dD , (3)

Total reflectivity is the result from summing the contributes ofhail and rain calculated separately.

For DSD calculation we adopted the Weather Research andForecast Model (WRF), a state-of the art NWP developed by aconsortium of research institutes including NOAA and NCAR[4]. The WRF can also provide the temporal evolution ofparameters based on a real scenario. This NWP gives usimportant parameters needed for the definition of an analytical,physical based Drop Size Distribution (DSD):

1) Hydrometeor mixing ratio [Kg/Kg]

2) Pressure [Pa]

3) Potential temperature [K]

4) Particle Number Concentration [particles/m3].

Additionally WRF provides the wind field used to computeDoppler shifts.

The DSD that we used in our computation is a Gammaprobability density function.

To compute the polarimetric RCS, we utilized a T-Matrixmethod. The T-Matrix method is the fastest exact technique forthe computation of non-spherical scattering based on a directsolution of Maxwell equations [5],[6]. Dielectric constants,particle orientation, diameter and the relationship betweendiameter and axial ratio are set as parameters to calculate theelectromagnetic scattering. Details on the generation of the 3Dreflectivity maps for every polarimetric channel can be foundin our previous work [7].

The received radar signal is then generated using a custo-mized version of Airborne Windshear Doppler RadarSimulation (ADWRS), extensively used by NASA in variouscampaigns [8].

The simulation input values include the radar systemsparameters, the cinematic characteristics of the airborneplatform, the antenna parameters and the scanning anglestrategy. Other inputs specify the phenomenon characteristicsin term of wind field and radar reflectivity. Last two variable isrepresented by a 3D data cube, described before. From both theinitial aircraft position and the initial antenna scan direction,the simulation consists of the generation of the instantaneousreceived signal. For each range bin, the amplitude and phase ofthe received signal can be seen as the coherent sum of anumber of contributions that came from volumetric scatteringmechanism.

III. 3D SIMULATION RESULTS

A. Description of the simulated scenarioSimulations were performed with the transponder

characteristics summarized in Table 1. It is worth nothing thatthe radar simulator can perform a full 360° scanning, but foravionic uses we can reduce this range to 180° or less. Theaircraft is positioned in the center (0,0), heading south at 150knots. The relatively low transmitted power is meant tosimulate the latest state-of-the-art solid-state GaAs radartransmitters equipping civil avionic weather radars, designed towork with such low peak power.

TABLE I. TRANSCEIVER CHARACTERISTICS

Transmitted frequency 9.353 GHz

Pulse length 1 s

PRF 6.5 kHz

Range resolution 150 m

Beam width 3°

Transmitted power 195 Watt

Antenna Gain 33 dB

Noise figure 4 dB

An area of about 1800 km2 in the Mediterranean Sea, closeto Barcelona, Spain, was selected, with a maximum height of8000 m. Figure 1 shows the profiles of hydrometeor mixingratios obtained from WRF at altitudes of 450 m, 1000 m and2000 m with a RGB mapping. Red indicates hail/graupel, blueindicates rain and mixed precipitation zones are in purple.

B. Results

Figures 2 to 4 show some simulation results regarding ZHH,ZDR and LDR, which accounts for the more or less pronouncedoscillations of hydrometeors. All these parameters are usefulfor classification between liquid and solid dangerous particles.

Figure 2 shows results for the lowest altitude level, wellunder the melting layer, dominated by rain. We can notice thepresence of a heavy storm characterized by strong reflectivity


echoes up to 53 dBZ, however we cannot distinguish if theseechoes are due to hail or rain. Analyzing ZDR, the radarretrieves values from 1 up to 3.6 dB in the storm core. Asexplained in section I, this behavior indicates a rain dominatedzone, as. Moreover, LDR level do not surpass -25 dB level,indicating small oscillations of particles during fall, which isanother characteristics of rain. Over -15 dB values appearusually where both Zhh and Zvh are very low, so the ratio issimilar.

Figure 3 represents an intermediate altitude where rain andhail are heavily mixed. As expected, total reflectivity levelremains the same as before, but we can appreciate variations inthe values of ZDR and LDR. ZDR begins to decrease steadilyreaching his top at 2.8 dB, while LDR rises up to a value of -21.2 dB. This behavior is typical of a mixed precipitation zone,but we can still detect rain presence in near the borders of thescanned area at (0,-20) and (-15,10) coordinates.

Where polarimetry shows its potential in detectingdangerous area is well shown in Figure 4. Once more, totalreflectivity level remains in the 55 dBZ range, but observationof ZDR and LDR supports the evidence of a hail dominatedzone. Maximum value of ZDR do not exceed 0.3 dB, and it evenhas negative value, -0.1 dB, which can be caused, other thanthe tumbling motion of hail, by the Mie scattering effects forlarger stones. LDR rise up to a value of -18/-17 dB, indicatinga very high signal power in the VH channel (see Eq. 2).

IV. CONCLUSIONS

It is clear that even in an uniform reflectivity phenomenon,in both its horizontal and vertical structure, polarimetric dataprocessing can provide useful information for featurediscrimination and thus to reduce risk due to solid particlesimpact. Even if the beamwidth is three degrees, combining thesignal received from partially overlapping azimuthal sectors itis possible to have information which permit to make a gooddiscrimination and resolve different scattering behaviour.Further studies will be conducted to evaluate returns from verylong distances. Long ranges suffer also from heavy attenuationwhich can be compensated using an additional polarimetricvariable, the Specific Differential Phase (KDP), that is also agood estimator for rainfall rate. This accurate risk assessmentis not possible with single-polarization avionic radar, so theonly action that is taken is making long detours, even if thephenomenon would pose no threats.

REFERENCES

[1] F. J. Yanovsky, “Evolution and Prospects of Airborne Weather RadarFunctionality and Technology”, 18th International Conference onApplied Electromagnetics and Communications, 2005.

[2] V.N. Bringi, and V. Chandrasekar, “Polarimetric Doppler WeatherRadar,” Cambridge University Press, 2004.

[3]Classification and Quantification Using Polarimetric Radar Data:Synthesis of Relations,” J. Appl. Meteor. 39, 2000, pp. 1341–1372.

[4] S.E. Koch, “The Use of Simulated Radar Reflectivity Fields in theDiagnosis of Mesoscale Phenomena from High-Resolution WRF ModelForecasts,” 32nd Conference on Radar Meteorology, 2005.

[5] P.C. Waterman, “Scattering by Dielectric Obstacles,” Alta Frequenza(Speciale), 1969, pp. 348–352., 1969.

[6] M. Mishchenko, L.D. Travis, and A.A. Lacis, “Scattering, Absorptionand Emission of Light by Small Particles,” Cambridge University Press,2nd ed., 2005.

[7] A. Lupidi, C. Moscardini, F. Berizzi, M. Martorella, "Simulation of X-Band Polarimetric Weather Radar Returns based on the WeatherResearch and Forecast Model", 2011 IEEE Radar Conference, KansasCity, 2011.

[8] Britt, C., L., Kelly, C., W., “User’s Guide for an Airborne DopplerWeather Radar simulation (ADWRS)”, Center for AerospaceTechnology, Tech. Rep. 7473/029-05S NASA, 2002.


1 (a)

1 (b)

1 (c)

Figure 1: Mixing Ratio: (a) 450 m (b) 1000 m (c) 2000 m altitude

2 (a)

2 (b)

2 (c)

Figure 2: 450 m altitude: (a) Total Reflectivity, (b) ZDR, (c) LDR


3 (a)

3 (b)

3 (c)


4 (a)

4 (b)

4 (c)



Principles of Utilization of Polarization Invariant Parameters for Classification and Recognition of

Distributed Radar Objects Part I. Simplest model of a distributed object

Victor N. Tatarinov, Sergey V. Tatarinov Radiodesign Department

Tomsk University of Control Systems and Radioelectronics Tomsk, Russian Federation [email protected]

Piet van Genderen IRCTR/MTSRadar

Delft University of Technology Delft, The Netherlands

[email protected]

Abstract—The paper is the first step towards the utilization of polarization invariant parameters for the classification and recognition of distributed radar objects. The simplest model of a distributed object is analyzed using the emergence principle and a generalization of interference laws. The experimental validation is presented.

Keywords- emergence principle; generalized interference laws; distributed radar objects; flight safety

I. INTRODUCTION

Research on the polarization properties of the electromagnetic field scattered by a distributed radar object with the aim to classify and recognize them, is closely connected with the definition of the polarization properties of the scattered field on the basis of the emergence principle and with the use of possible relations between polarization properties of the constituent parts of the complex radar object. The research leads to the concept of “generalized interference laws”. The paper presents the results of the theoretical and experimental investigation of polarization power parameters at the scattering of electromagnetic fields by two-point radar objects. Such a two-point radar target is the simplest model of a distributed object.

II. THE DEFINITION OF A COMPLEX RADAR OBJECT. EMERGENCE PRINCIPLE

We will use the definition of a complex radar object using the Stratton-Chu integral [1] , which allows to represent a field, scattered by this object, as the sum of waves scattered by elementary scatterers (“bright” or “brilliant” points), constituting the complex object. For the case when every elementary scatterer is characterized by its scattering matrix

( ); , 1, 2ikmS i k = then the complex vector of the scattered

field can be defined in the form

( ) ( ) ( )00

10

exp 2exp 2

4

Nik

S m mm

j kRE S E j kX

R == − −θ θ

π (1)

where mX is distance between the center of gravity of the object and the mth bright point, 0R is the distance between the radar and the center of gravity of the object, θ is the aspect

angle of the object and 0E is the complex vector of the initial wave. It is necessary to indicate here that the expression (1) represents the polarization properties of all individual scatterers, which together form the large distributed radar object. Unfortunately, the properties of a large system in principle cannot be derived by simply adding the properties of the elementary parts of the system. The properties of the integral system properties appear after considering the relations between its elements. These relations lead to the “emergence” of new properties which do not exist for every element separately. The concept of “emergence” is one of the main definitions of the systems analysis [2]. So, we will try to find the polarization properties of the electromagnetic field scattered off a complex radar object on the basis of the emergence principle, using the possible relations between the polarization properties of all elementary scatterers constituting a complex radar object. We will take into account that these elementary scatterers cannot be resolved by radar.

III. ANGULAR DISTRIBUTION OF THE SPACE FREQUENCIES AND STOKES PARAMETERS AT THE SCATTERING BY A

COMPLEX OBJECT

Let us consider now the dependence of the polarization parameters of the scattered field both on the spatial distribution of the scatterers and on their possible interactions. We will consider the simplest complex (distributed) radar object, consisting of two closely connected scatterers A and B (reflecting elliptical polarizers), which cannot be resolved by the radar. These scatterers are separated in space by a distance l and are characterized by the scattering matrices in the Cartesian polarization basis

11

2

00a

Sa

= , 12

2

00bS

b= (2)


Fig.1. The scattering geometry for the two-point radar object

We will consider coherent scattering. The geometry is shown in the Fig. 1. Here the distances

1 2,R R between the scatterers and an arbitrary point Q in the far field can be written as

2,1 0 00,5 sin 0,5R R l R l≈ ± ≈ ±θ θ under the condition

00,5l R<< . Using these expressions, we can find the Jones vector of the scattered field for the case of the radiated signal having a linear polarization at an inclination angle of 45° . It is should be mentioned here that we are using the Cartesian (linear) polarization basis both for the scattered matrices and for the Jones vector of the scattered field.

( ) ( ) ( )( ) ( )

1 1

2 2

exp exp22 exp expS

a j b jE

a j b jξ ξ

θξ ξ

+ −=

+ −(3)

where klξ θ= . The angular dependence of the polarization-energetic response functions in the form of the Stokes parameters 0 3,S S is

( ) ( ) ( ) ( ) ( )( ) ( ) ( ) ( ) ( )

0

3

;

[ ].X X Y Y

X Y Y X

S E E E E

S i E E E E

θ θ θ θ θ

θ θ θ θ θ

∗ ∗

∗ ∗

= +

= −

The expanded form of the energetic response function ( )0S θcan be found as

( )0 0 00,5 a bS S S= + +θ

( )2 2 2 21 1 2 2 1 2 1 2 1 2 1 2 1cos 2a b a b a a b b a a b b∗ ∗ ∗ ∗+ + + + +ξ η

(4)

where ( ) ( ){ }1 1 1 2 2 1 1 2 2Im / Rearctg a b a b a b a bη ∗ ∗ ∗ ∗= + + and 2 2

0 1 2aS a a= + , 2 2

0 1 2bS b b= + . The values 0 0,a aS S are the

Stokes zero-parameters of the elementary scatterers “a” and “b”.The polarization-angular response function ( )3S θ can be found as

( )3 3 30,5 a bS S S= +θ +

( )2 2 2 21 1 2 2 1 2 1 2 1 2 1 2 22 ( ) sin 2a b a b a a b b a a b b∗ ∗ ∗ ∗+ + − + +ξ η

(5)

where ( ) ( ){ }2 1 2 2 1 1 2 2 1Im / Rearctg a b a b a b a bη ∗ ∗ ∗ ∗= − − and

( )3 1 2 1 20.5aS j a a a a∗ ∗= − − , ( )3 1 2 1 20.5bS j b b b b∗ ∗= − − are the 3-rd

Stokes parameters of the elementary scatterers “a” and “b”.

The angular harmonic functions [ ]cos ... , and [ ]sin ... in the expressions (4) and (5) represent the influence of the spatial separation l on the distribution of the polarization-energetic parameters of scattered field in the far zone. The derivative of the full phase ( ) 2 kklψ θ θ η= + ( 1, 2k = ) of the angular harmonic functions along the angular variable is the space frequency:

[ ]1 222SP k

d lf kld

θ ηπ θ λ

= + = (6)

Thus, the space frequency in the distributed radar object theory equals twice the distance between the elementary scatterers constituting the radar object, normalized to the wave length. Next we will analyze the amplitudes of the angular harmonic functions [ ]1cos 2klθ η+ , [ ]2sin 2klθ η+ in order to assess the impact of the polarization properties of the elementary scatterers on the polarization-energetic parameters of the field scattered by the complex radar object. Let us write the polarization ratios 2 1/AP a a= and 2 1/BP b b= which are characterizing the point radar objects A and B on the complex plane of radar objects [3]. Using the method of the stereographic projections, we can find the spherical distance between the points ,A BS S , laying on the surface of the Riemann sphere having unit diameter, which are connected with the points ,A BP P on the radar object’s complex plane. The coordinates of the points ,A BS S on the surface of the sphere are

2 2 2 2

1 2 3Re /(1 ); Im /(1 ); /(1 )X P P X P P X P P= + = + = +and the spherical distance between these points can be found to be:

2 2( , )

1 1

A BS A B

A B

P PS S

P P

−= =

+ +ρ

( )2 2

2 21 1

A B A B A B

A B

P P P P P P

P P

∗ ∗+ − +=

+ +

(6)

where A BP P− is the Euclidian metric on the complex plane of radar objects. After substitution of the polarization ratios

2 1/AP a a= and 2 1/BP b b= into the expressions (6) we can write

( )( )2 2 2 21 1 2 2 1 2 1 2 1 2 1 2

2 2 2 21 2 1 2

( )( , )S A B

a b a b a a b b a a b bS Sa a b b

∗ ∗ ∗ ∗+ − +=

+ +ρ (7)

The value

( )( )2 2 2 21 1 2 2 1 2 1 2 1 2 1 2

2 2 2 21 2 1 2

( )a b a b a a b b a a b bDa a b b

∗ ∗ ∗ ∗+ − +=

+ + (8)

0,5l 0,5l

1R2R0R

θ

1S2S


is the so-called polarization distance between two waves (or radar objects), having different polarizations [5]. When the waves having coincident polarizations, ( A BP P= ) can be characterized by the polarization distance value 0D = and the waves having orthogonal polarizations ( 1/B AP P∗= − ) can be characterized by the polarization distance value 1D = . Thus, it follows from (7) and (8) that

( )( )2 2 2 2 2 2 2 21 1 2 2 1 2 1 2 1 2 1 2 1 2 1 2( )a b a b a a b b a a b b D a a b b∗ ∗ ∗ ∗+ − + = + + .

We can also use the so-called polarization proximity value Nthat can be defined as 1N D= − . Then

( ) ( )2 2 2 21 1 2 2 1 2 1 2 1 2 1 2

2 2 2 21 2 1 2

1a b a b a a b b a a b bN D

a a b b

∗ ∗ ∗ ∗+ + += − =

+ + (9)

The waves with coinciding polarizations ( A BP P= ) can be characterized by the polarization proximity value 1N = and the waves having orthogonal polarizations ( 1/B AP P∗= − ) can be characterized by the polarization proximity value 0N = .Then we can write

( )( )2 2 2 2 2 2 2 21 1 2 2 1 2 1 2 1 2 1 2 1 2 1 2a b a b a a b b a a b b N a a b b∗ ∗ ∗ ∗+ + + = + + .

If we compare the amplitudes of the space harmonic oscillations with the expressions (7) and (9), we can see that the expressions (4) and (5) can be rewritten as

( ) ( )0 0 0 0 0 10,5 2 cos 2a b a bS S S S S N= + + +θ ξ η (10)

( ) ( )3 3 3 0 0 20,5 2 sin 2a b a bS S S S S D= + + +θ ξ η (11)

We can consider these expressions as generalized interference laws [4]. It follows from the expression (10) that the orthogonally polarized waves cannot give an interference picture in case the polarization proximity value 0N = .However, the expression (11) demonstrates that in this case the third Stokes parameter will have the maximal value of this interference picture visibility.It follows from expressions (10), (11) that every Stokes parameter has some constant component, which is defined by the respective Stokes parameters of both objects (“a” and “b”), and space harmonics function [ ]1cos 2klθ η+ , [ ]2sin 2klθ η+ ,

having amplitudes 0 0a bS S N , 0 0

a bS S D and space initial phase kη . So, the polarization-energetic properties of complex radar object cannot be found using only the properties of its individual elements. The properties of the integral system appear by taking the relations between the individual elements into account. These relations in our case are the polarization distance and the polarization proximity. The use of these values leads to the “emergence” of new properties which did not exist for every element separately. We define an instantaneous visibility of the generalized interference law as

( ) ( )( ) ( )

0 00 0

0 0 0 0

2 .A BMAX MIN

MAX MIN A B

S SS SW N

S S S Sθ θθ θ

−= =

+ +(12)

It can be seen that the equation (12) is coinciding with the well known expression for the Fresnel-Arago interference law

( ) ( )( ) ( )

1 212

1 2

2 ,MAX MIN

MAX MIN

I I I IW

I II Iθ θ

γθ θ

−= =

++

where 1 2,I I are the integrated powers (energies) of the waves and 12γ is a degree of coherence. If 1 2I I= then the visibility of the interference law is defined by the degree of coherence of second order. So we can state that from a physical point of view the parameter N can be considered as a polarization coherence parameter, which defines the proximity of the polarization states of elementary scatterers, and in the same way a degree of coherence of stochastic waves is summarized. In this case we have an “instantaneous” value of the polarization coherence, while at the same time the coherence degree 12γ is the correlation value.

IV. EXPERIMENTAL INVESTIGATIONS OF THE POLARIZATION-ENERGETIC PROPERTIES OF

ELECTROMAGNETIC WAVES SCATTERED BY A TWO-POINT RADAR OBJECT

A measurement campaign to investigate jointly the generalized Fresnel–Arago interference laws and the polarization-energetic properties of the electromagnetic field scattered by reflecting interferometers (man-made radar objects consisting of two elements) was realized by the International Research Centre for Telecommunications and Radar of TU Delft [5]. In this paper a small part of the results is presented and interpreted from the point of view of the generalized Fresnel–Arago interference laws and the emergence principle with respect to power and polarization harmonics ( ) ( )0 3,S Sθ θ , corresponding to the space frequency caused by the distributed radar object used in the campaign. A collection of two–elements man–made distributed radar objects with known polarization properties of their elements was used in the campaign. The difference between the properties of the various elements constituting the distributed radar objects leads to different values of the polarization proximity or polarization distance of these elements. The following combinations of two– elements man–made distributed radar objects were used: 1).Two empty trihedral corner reflectors ( 1; 0N D= = );2).Two trihedrals, where the first one was empty and the second one was fitted with a linear polarizer consisting of a special polarizing grid. ( 0.5; 0.5N D= = );3). Two trihedrals, where the first one was empty and the second one was fitted with an elliptic polarizer consisting of a special polarizing grid. The transmission coefficients along the OX and OY axes are 0.5Y Xb b= and the mutual phase shift between the polarizer’s eigen axes is / 2XYϕ π=( 1; 0.5;A BP P j= = 0.5;N = 0.5D = ); This object is presented in the Fig. 2.


Fig. 2. Two-point radar object N3

4).Two trihedrals, where the first one was fitted with the linear polarizer and the second was fitted with an elliptic polarizer ( 0; 0.5; 0.8; 0.2A BP P j N D= = = = ). The phase centers of the trihedrals were separated by 100 cm, while the wavelength of the radar was 3 cm. For these parameters the space frequency and space period are

12 / ( ) ,SPf l Radλ −= 0,015SPT Rad= (or 0.855 ).° The mechanical construction, on which the trihedrals were mounted, rotated with an angular step 0,25° .When the object includes the trihedral with the elliptic polarizer and the empty trihedral (combination 3 ), we can find the theoretical estimation of the polarization proximity and distance as 0.5.N D= = In the Fig.3a,b the experimental angular harmonics functions (generalized interference pictures) ( ) ( )0 3,S Sθ θ are shown. It follows from these figures

that the visibility for interference picture ( )0S θ is 0 0,3W ≈corresponding to a polarization proximity 0 0.54N = (note that the theoretical estimation is N=0.5). The visibility for ( )3S θ is 3 1W = , corresponding to a polarization distance

0.5D = . For the system including the trihedral arranged by the linear polarizer and empty trihedral (object N2), we can

00,10,20,3

1 3 5 7 9 11 13 15 17Fig.3a. Generalized interference law

for the Stokes parameter ( )0S θ (object N3)

00,10,20,3

1 3 5 7 9 11 13 15 17Fig. 3b. Generalized interference law for the Stokes parameter

( )3S θ (object N3)

00,10,20,3

1 3 5 7 9 11 13 15 17Fig.4a Generalized interference law


00,10,20,3

1 3 5 7 9 11 13 15 17Fig. 4b. Generalized interference law


find the theoretical estimation visibility values 0 0,66;W = 3 1W = that correspond to polarization proximity

values 0 0 3 30.82; 1N W N W= = = = . In the Fig.4a,b the

angular harmonics functions ( ) ( )0 3,S Sθ θ for this situation are shown. The experimental estimation based on Fig.4a,b gives us

0 30.85; 1N N≈ = what is the closely coinciding with the theoretical estimation.

V. CONCLUSIONS

The results presented demonstrated the phenomenons which occur when electromagnetic waves are coherently scattered by two-point radar objects. These results can be used for preliminary classification of complex radar objects in the flight safety problem

REFERENCES

[1] J.A.Stratton, L.J.Chu. Diffraction theory of electromagnetic waves. Phys. Rev, v.56, pp 308- 316.

[2] F.I.Peregudov, F.P.Tarasenko. The principles of systems analysis. Tomsk, 2001, 350 p. (In Russian)

[3] V.N. Tatarinov, S.V. Tatarinov, L.P. Ligthart. An introduction to radar signals polarization modern theory. Tomsk State University Publ House, 2006, vol.1 380 pp.

[4] V.N. Tatarinov, S.V. Tatarinov. “A Generalization of Fresnel-Arago Interference Laws”. Proc. of Int. Conf. SoftCOM ’09, Split-Hwar, Croatia, Sept. 2009

[5] V.N. Tatarinov, S.V. Tatarinov, P. van Genderen, D.Tran, P.Usov, J.Zijderveld. The Reports IRCTR-S-028-03 and IRCTR-S-029-04.


Principles of Utilization of Polarization Invariant Parameters for Classification and Recognition of

Distributed Radar Objects Part II. Multipoint model and correlation theory

Victor N. Tatarinov, Sergey V. Tatarinov Radiodesign Department

Tomsk University of Control Systems and Radioelectronics Tomsk, Russian Federation [email protected]

Piet van Genderen IRCTR/MTSRadar

Delft Technology University Delft, The Netherlands

[email protected]

Abstract— The paper is the second step towards the utilization of polarization invariant parameters for classification and recognition of distributed radar objects. A the theory on the statistical properties of the polarization parameters of the scattered field is established and confirmed by the experimental data

Keywords- Stokes parameters; angular distribution;autocorrelation function; space spectra of distributed objects

I. INTRODUCTION

This paper demonstrates that the interference process of the field scattered from multi-point random complex radar objects (RCRO) leads to polarization-energetic speckles. The polarization-energetic response function of an RCRO can be considered to be a collection of space harmonics. Every space harmonic of this collection is initiated by one pair out of many pairs, which can be formed by the multi-points constituting the scattering RCRO. Every space harmonic will have an amplitude, which will be determined by the value of the proximity (or distance) of the polarization states of the points involved in the respective pair. The positions of the elementary scatterers composing the RCRO are stochastic and we have a random number of interfering pairs. The polarization proximity of each pair also is a stochastic parameter, and thus, even when the spatial separation between points in a pair is the same, we will have a classical stochastic process at each change of the aspect angle. Our approach to the problem is novel and it is formulated like this for the first time.

II. THE POWER ANGULAR DISTRIBUTION OF THE ELECTROMAGNETIC FIELD SCATTERED BY A DISTRIBUTED

OBJECT

Let us consider first the geometry at the scattering by one of the scatterers of the multi-point (complex) radar object (fig.1). For the case of coinciding linear polarizations both for transmission and reception we can write the field scattered by a point scatterer Iσ (where Iσ is the Radar Cross Section

(RCS) of this scatterer) , observed in some point Q in the far field as

( )0

exp 24

IS I

I

j kRE E

Rσ

π= − (1)

where IR is the distance between the scatterer at IX and the point Q at X (ref. Fig.1) ; 0E and SE are the initial and the scattered field electrical vectors respectively and k is the wavenumber, 2k = , being the wavelength. Taking into account that

0I IR R X θ≈ − (2)

we can write the scattered field in the point Q in the form

( ) ( ) ( )00

0

exp 2exp 2

4S I I

j kRE E j kX

Rθ σ θ

π= − − (3a)

When all scatterers are characterized by a scattering matrix ( ); , 1,2il

IS i l = , the complex vector of the scattered field will be connected with the complex vector of the initial field as

( ) ( ) ( )00

0

exp 2exp 2

4il

S I I

j kRE S E j kX

R= − −θ θ

π (3b)

Let us now consider the formation of the distribution of the polarization-energetic parameters of the electromagnetic field resulting from the interference process at the scattering by the RCRO. To this end we will analyze the scattering process by the multi-point RCRO. Without loss of generality we will assume that the point scatterers constituting the RCRO, are located on a line (see Fig.1). For the example of Fig.1 we will find that the electrical vector of the field, scattered by the 4-points complex object, observed in a point Q located in the far field for the case of coinciding linear polarization both for transmission and reception is:


( ) ( ) ( )0 01 1

0

exp 2{ exp 2

4S

j kR EE j kX

Rθ σ θ

π= − − +

( )2 2exp 2j kXσ θ+ − +

( ) ( )3 3 4 4exp 2 exp 2 }.j kX j kXσ θ σ θ+ − + −

(4)

Then we can find the instantaneous distribution of the power of the scattered field in space as a function of the positional angle θ as:

( ) ( ) ( ) ( )1 1{ exp 2S SP E E A j kXθ θ θ σ θ∗= = − +

( ) ( )2 2 3 3exp 2 exp 2j kX j kXσ θ σ θ+ − + − +

( ) ( )4 4 1 1exp 2 } { exp 2j kX j kXσ θ σ θ+ − × +

( ) ( )2 2 3 3exp 2 exp 2j kX j kXσ θ σ θ+ + +

( )4 4

1 2 3 4

exp 2 }j kX+= + + + +

σ θσ σ σ σ

( ) ( )1 2 12 1 3 132 cos 2 2 cos 2kd kdσ σ θ σ σ θ+ + +

( ) ( )1 4 14 2 3 232 cos 2 2 cos 2kd kdσ σ θ σ σ θ+ + +

( ) ( )2 4 24 3 4 342 cos 2 2 cos 2kd kdσ σ θ σ σ θ+ +

(5)

So, the instantaneous distribution of the power of the scattered field in space as a function of the positional angle θ is formed by the sum of the radar cross section of the elementary scatterers (4 terms) plus 6 cosine oscillations. It can be seen that all of these cosine terms are caused by the interference effect between the fields scattered by all pairs of elementary scatterers constituting the RCRO. The number of these pairs can be found by the binomial coefficient

( )!

! !NM

MCN M N

=−

where M is the total number of points, and N is the number of points in each combination. In our case, where 4M = , 2N = ,we have 2

4 6C = . So, the angular response function of the complex radar object considered will include 6 space harmonic functions as a result of the interference, summarized in the expression (5). There the values

12 1 2 13 1 3 14 1 4; ; ;d X X d X X d X X= − = − = −

23 2 3 24 2 4 34 3 4; ; d X X d X X d X X= − = − = −represent the spatial distance between the scattering elements for every interfering pair. The space harmonic function

( )cos 2i l ilkdσ σ θ corresponds to the definition in [1]. In accordance with this definition, the harmonic oscillation in space having the shape ( )cos 2kdθ is defined by the full phase

( ) 22 2kd dπψ θ θ θλ

= = , the derivative of which is

1 22 SP

d d fdψ

π θ λ= = . It represents the space frequency with

dimension 1Rad − . The period 1/ / 2SP SPT f dλ= = having

the dimension [ ]Rad corresponds to this frequency. So, the full power distribution of the field scattered by a complex radar object, is the sum of the interference patterns formed by a collection of elementary two-points interfering scatterers. Thus, we can write the random angular representation of the scattered power, depending on the positional angle as

( ) ( )2

1 1

2 cos 2M C

m i l ilm

P kd=

= +θ σ σ σ θ (6)

where 2MC C= is number of combinations, and M is the total

number of elementary scatterers constituting the RCRO.

III. THE ANGULAR DISTRIBUTION OF THE STOKES PARAMETERS OF THE ELECTROMAGNETIC FIELD SCATTERED

BY A DISTRIBUTED OBJECT

It was demonstrated in [1] that the angular distribution of the Stokes parameter 0 3andS S of the electromagnetic field scattered by a two-point distributed object has the form

( ) ( )( ) ( )

0 0 0 0 0

3 3 3 0 0

2 cos 2 0.5 ;

2 cos 2 0.5 .

a b a bab

a b a bab

S S S S S N kl

S S S S S D kl

θ θ ϕ

θ θ ϕ

= + + +

= + + −(7)

It follows from expression (7) that the space harmonic functions ( )cos 2klθ η± have amplitudes 0 0

a babS S N

and 0 0a b

abS S D . Here the values ,ab abN D are respectively the proximity and distance of the polarization states of the elementary scatterers of the distributed object [1, 2]. Taking this into account, we can write the angular distribution of the Stokes parameters of the field scattered by a random complex radar object as the sum of the generalized interference patterns, which are formed by a collection of elementary two-points interfering scatterers (see Fig.1):

2X 3X 4X1X

4R3R

2R1R

0R

0

Q

θ

4R

XFig. 1. The scattering geometry for multi-point radar object


( ) ( )0 0 0 01 1

2 cos 2M C

mi l il il il

m

S S S S N kd=

= + +θ θ η (9a)

( ) ( )3 3 0 01 1

2 cos 2M C

mi l il il il

m

S S S S D kd=

= + +θ θ η (9b)

where 2MC C= is the total number of combinations. The

amplitudes of the space harmonics and the initial space phases of these harmonics will be stochastic values. Thus the further analysis must be based on statistics.

IV. A THEORETICAL DEFINITION OF THE AUTOCORRELATION FUNCTION OF THE ANGULAR

DISTRIBUTION OF THE STOKES PARAMETERS OF THE SCATTERED FIELD. SPACE SPECTRA

Now we will develop a theoretical form of the autocorrelation function of the angular distribution of the Stokes parameter

( )3S θ of the scattered field. Since we would like to find the autocorrelation function (not the covariance function!), we must eliminate a random

constant term 31

Mm

mS

=

from the stochastic function ( )3S θ to

ensure a zero mean value. Taking into account that the value

31

Mm

mS

=

can be a non-stationary stochastic function, the

average must be found using a sliding window. After elimination of the non-stationary mean value and subsequent normalization, we can write the stochastic function ( )3S θ as

( ) ( )31

cos 2C

il il ilS D kd= +θ θ η (10)

Let’s suppose that the function (10) is a stationary stochastic function. Then its autocorrelation function can be found as

( ) ( ) ( ){ }1 2SB M S Sθ θ θΔ = =

[ ]1 1

cos 2C C

N L NN L

M D D kd θ η= =

= + ×

[ ]}cos 2 ( )Lkd θ θ η× + Δ +

(11)

Here the space harmonics amplitudes D and the space initial phase η are random values, which can be characterized by a two-dimensional probability distribution density

( )2 ,W D η , M is the averaging operator and 1 2θ θ θΔ = − .

We will suppose that the random amplitudes and phases are independent variables. Then the two-dimensional probability distribution can be presented as the product of two one-dimensional distributions:

( ) ( ) ( )2 1 1,W D W D Wη η=

Using the orthogonality condition

( ) ( ) 1cos cos

0N LSP SP

forN Ld

forN Lθ θ θ

=Ω Ω =

≠we can rewrite the expression (11) as

( ) ( )2

1

C

S NN

B M Dθ=

Δ = ×

[ ] [ ]}cos 2 cos 2 ( )N Nkd kdθ η θ θ η× + + Δ +(12)

Considering that the initial stochastic realization ( )3S θ is a function of the random variables and D θ , we will use for the definition of the autocorrelation function of this realization the expression for the mean value of a function of two random variables:

( ){ } ( ) ( )1 2 1 2 2 1 2 1 2, , ,M y x x y x x W x x dx dx∞ ∞

−∞ −∞

=

Using this expression we can write the autocorrelation function (12):

( ) ( ) [ ]2

1cos 2

C

S N NN

B D kdθ θ η∞ ∞

= −∞ −∞

Δ = + ×

[ ] ( ) ( )2cos 2 ( ) ,Nkd W D d D dθ θ η η η× + Δ +(13)

For the calculation of the double integral in (13) we will use the condition ( ) ( ) ( )2 1 1,W D W D Wη η= mentioned before.

Let’s suppose also that the random phase has a uniform probability distribution density on the interval ( ),π π− , i.e.

( ) 1/ 2W η π= . A probability distribution density for the random amplitude D can be preassigned, however for all cases it has to be one-sided. Now we will transform the product of the cosine functions and substitute it into (13) together with the probability distributions of the random variables and D θ :

( ) ( )1 2 31

C

SN

B I I Iθ=

Δ = + + (14)

Let’s now evaluate the integrals 1 2 3, ,I I I . The first integral gives

( ) ( ) ( ) ( )2

1 10

0.5 cos 22 N NI D kd W D d D d

π

π

θ ηπ

∞

−

= Δ =

( )0,5 cos 2N ND kd θ= < > Δ

(15)

where ND< > is the mean value of the polarization distance, which was found by averaging over the statistical ensemble of random values ND for all space harmonics having the space frequency 2 /N

SP Nf d λ= .The second integral 2I can be evaluated using the condition

cos0

sind

π

π

ηη

η−

=

and the result is

( ) ( )2

0

0.5 cos 2 22 N ND kd

π

π

θ ηπ

∞

−

+ ×

( ) ( ) ( )1cos 2 0.Nkd W D d D dθ η× Δ =

It can be shown that the third integral, containing the sinus functions, is zero as well: 3 0I = . Thus, we can write the theoretical form of the autocorrelation function of the angular distribution of the Stokes parameters of the scattered field as:


( ) ( )1

cos 22

CN

S NN

DB kdθ θ

=

< >Δ = Δ (16)

Taking into account that every term of the sum in (16) is the autocorrelation function for an isolated space harmonic oscillation ( ) ( )cos 2N N N NS D kdθ θ η= + having random amplitude ND and random initial space phase Nη , i.e.

( ) ( )cos 22

NSN N

DB kdθ θ< >

Δ = Δ (17) it can be seen that the autocorrelation function of the stochastic realization of the Stokes parameters is the sum of the individual autocorrelation functions of all space harmonics which are contributing to this stochastic realization:

( ) ( )1

C

S SNN

B Bθ θ=

Δ = Δ (18)

Let’s now develop complex radar object’s averaged space spectra using expression (18) for the autocorrelation function of the polarization-angular response. The power spectrum in the case of isolated space harmonics can be found as the Fourier transform of the above the autocorrelation function (17)

( ) ( ) ( ) ( )expSP SN SPP B j dθ θ θ∞

−∞

Ω = Δ − Ω Δ Δ =

( ) ( )0.5 [ ]N NN SP SP SP SPD δ δ= < > Ω − Ω + Ω + Ω

(19)

where 22 2SP SPdfπ π

λΩ = = is the space frequency. The

spectral lines are located at the distances NSP±Ω from the

origin of the co-ordinate system and their positions are defined by the space frequency 2 /N

SP Nf d λ= of a two-point radar object. This space frequency depends on the spatial separation of two reflectors distributed in the space. The intensity of the power spectral lines is determined by the polarization distance between the polarization states of two scatterers forming the radar object. The full space spectra of the stochastic polarization-angular response, i.e. the Fourier transform of the autocorrelation function (8) is

( ) ( ) ( )1

0,5 [ ]C

N NSP N SP SP

NP D δ δ

=

Ω = < > −Ω + +Ω (20)

So, the power spectra of the polarization angular response function have a discrete form. It is caused by the discrete structure of the RCRO. Besides, man-made distributed radar objects have a finite extension. In this context we have to emphasize that the power spectra of radar objects have a limited character.

V. EXPERIMENTAL INVESTIGATIONS OF THE ACF AND SPACE SPECTRA

The experimental autocorrelation functions (ACF) and space spectra of the stochastic polarization-angular response of a rotated complex radar object (it is a Caterpillar vehicle, a heavy construction machine) are shown in Fig.2 and Fig.3.

Fig.2 shows the autocorrelation function in the angular interval 20± ° w.r.t. the object’s board (dashed line) and the autocorrelation function over the same interval w.r.t. the stern of the object (solid line). The measurements in these directions allow us to consider the difference in the radar object’s space spectral bands when it is observed in areas perpendicular to the board (dashed line) and to the stern of the object (solid line). It can be seen from Fig. 3 that the RCRO’s mean power spectra have a two-mode form. It shows that the so-called equivalence principle can be used “on the average” in order to describe a model of a random complex radar object [3] as two distributed scatterers in space.

-1-0,5

00,5

1

Fig. 2. Autocorrelation functions of RCRO stochastic polarization-angular response

-0,5

0

0,5

1

Fig.3. Mean power space spectra of RCRO

VI. CONCLUSION

The results of this paper confirm that we can consider the formation of the polarization–power parameters of the electromagnetic field scattered by an RCRO as an interference process. This fact allows us to find both the autocorrelation function and the RCRO space power spectra of the polarization-angular stochastic response. It gives us the possibility for recognition and classification of distributed radar objects.

REFERENCES

[1] V. N. Tatarinov, P. van Genderen, S. V. Tatarinov. “Principles of Utilization of Polarization Invariant Parameters for Classification and Recognition of Distributed Radar Objects. Part 1. Simplest Model of a Distributed Object”. Proc. of the Int. Conf. ESAV’2011.

[2] V.N. Tatarinov, S.V. Tatarinov, L.P. Ligthart. An introduction to radar signals polarization modern theory. Tomsk State Univ.. Publ. House, vol.1 2006, 380 pp. (In Russian)

[3] S.V.Tatarinov, L.P.Ligthart, V.N.Tatarinov. “The use of an equivalence principle “on the average” for a statement of definition of an random complex radar object model”. Proc. Of MIKON’2000, Wrozlaw, Poland, Vol 2, pp. 12-17


Enhancing sensitivity for emitter geolocationSession 4.1 page 89

New solution to enhance the security in Air Traffic ControlSession 4.2 page 95

Solving the Data Link bottleneck for MPEG LocationSession 4.3 page 101

Parasitic Doppler effect in passive location

Session 4.4 page 107

An in air passive acoustic surveillance system for air traffic control



Enhancing sensitivity for emitter geolocation

Göran Tengstrand, Viktor Andersson, Peter Hultman Saab Electronic Defence Systems

Järfälla, Sweden

Jean-François Grandin, Luc BosserThales Systemes Aeroportes

Elancourt, Cedex France

Dario Benvenuti Elettronica Rome, Italy

Börje Andersson, Anders Johansson Swedish Defence Research Agency

Linköping, Sweden

Abstract Angle of arrival (AOA), time of arrival (TOA) and frequency of arrival (FOA) can be measured for a signal from multiple platforms. By combining such measurements it is possible to obtain high accuracy emitter position estimates. This requires a data link with low latency and sufficient data-rate and synchronization of the platforms in space, time and search pattern. Typically several of the platforms will have to make their measurements in the radar sidelobes which requires very high receiver sensitivity. This paper focuses on discussing how the sensitivity can be improved using antenna gain or signal processing.

Keywords: Multi-platform emitter geolocation(MPEG), angle of arrival (AOA), time difference of arrival (TDOA), frequency difference of arrival (FDOA), data link, sensitivity, electronic support measures (ESM).

I. INTRODUCTION

Fire control radars usually are active only for short time periods and represent severe threats to fighter aircraft. Using electronic support measures (ESM) receivers the threat signals can be detected and measured. It would be of a high tactical value if these ESM receivers had the capability to obtain a high accuracy emitter position estimate almost instantaneously.

In order to obtain accurate position estimates of radar emitter it is crucial to have accurate position and time synchronization among the cooperating platforms. Clocks and platform positions can be synchronized with a theoretical relative accuracy of the order of 10 ns and 5 m using common view (CV) GPS (see Parkinson and Spilker [1]). Furthermore, precision crystal and rubidium clocks are now commercially available mitigating the requirements for precise clock synchronization. Reference emitters with well-known positions can also be a complement to GPS for synchronization of clocks and platform positions.

The time of arrival (TOA) of intercepted radar pulses can typically be measured with a precision of around 10 ns. The phase change of a signal during a 10 ms radar illumination typically can be measured with an accuracy of around 20°which corresponds to a frequency measurement accuracy of around 10 Hz.

Assume that the time difference of arrival (TDOA) of a pulse to two ESM systems can be measured with an accuracy of the order of 15 ns which corresponds to a distance difference of 4.5 m. Using multi-platform time difference of arrival (MP-

TDOA) with an ESM platform separation of 1 km the bearing to the emitter then can be obtained with an accuracy of around 0.3° in the normal direction to the platform separation line. With more than two platforms the 2D emitter position can be estimated. An introduction to TDOA emitter geolocation can be found in Wiley [2].

For multi-platform frequency difference of arrival (MP-FDOA) the orientation of the line of positions (LOP) and the accuracy is strongly dependent on the platform relative trajectories. Assume that two ESM platforms move 1 km after each other with a speed of 300 m/s and that the frequency difference of arrival to the platforms can be determined with an accuracy of around 10 Hz at 10 GHz. In the normal direction to the line between the platforms this corresponds to a bearing difference of 0.06°. At a range of 10 km from the emitter the range can then be estimated with an accuracy of the order of 100 m. FDOA geolocation is discussed in Wiley [2].

In order to obtain a good range estimate the measuring ESM platforms must have a rather large separation. Measurement of TOA and frequency on the same pulses implies that some of the ESM platforms will perform their measurements in the radar sidelobes which requires a very high sensitivity. The objective of this contribution is to discuss how such a high sensitivity can be obtained and how high accuracy measurements still can be obtained.

In section II ESM receiver sensitivity requirements are motivated from a scenario example. With a channelized receiver with narrowband receiver channels a basic good sensitivity can be obtained. The use of antennas and signal processing in order to improve the sensitivity is discussed in section III and section IV, respectively. Methods to measure signal parameters for weak signals are then analyzed in section V. The conclusions are finally summarized in section VI which is followed by a reference list.

II. SENSITIVITY REQUIREMENTS

A modern fire control radar using pulse compression can be expected to have an output power Ptr = 1 kW and a mainbeam antenna gain Gtr = 35 dB. The effective aperture area of an ideal isotropic antenna at a frequency f = 10 GHz is given by Aiso = λ2/(4π) = 0.72 cm2. At a range R = 20 km the power received by such an ideal isotropic antenna is then given by Prec= PtrGtrAiso/(4πR2) = -43 dBm. The sidlobe antenna gain of a radar can be expected to be 35 – 40 dB below the mainlobe

The authors acknowledge support from the European Defence Agency (EDA), French DGA, Swedish FMV and Italian MOD which have contributed to the funding of this study under EDA contract B-0055-IAP2-ERG.


antenna gain. In a sidelobe of the radar in our scenario example the power level can hence be expected to be down to around -80 dBm for an ideal isotropic antenna.

Consider a channelized receiver (see Tsui [3]) with B = 20 MHz channel bandwidth and a noise and loss factor F = 15 dB. Then the sensitivity is given by Pmin = kTFB⋅SNRmin = -73 dBm. Here kT = -174 dB(mW/Hz) is the Boltzmann’s constant and a reference temperature and SNRmin = 13 dB is a minimal signal to noise ratio. The boresight gain of a wideband widelobe antenna is around 0 dBi and around -20 dBi at 90° from boresight.

In order to detect and measure important threat radars in their sidelobes an extra sensitivity improvement of at least around 10 dB is required. The sensitivity can be improved by using a directional antenna, two antennas, signal integration or a matched filter. Different methods to detect weak signals are also discussed in Tsui [4] and Pace [5].

III. USE OF ANTENNAS

A. Directional antennas A wideband phased array antenna covering a frequency 3:1

ratio with 8×8 elements could be a suitable alternative to obtain extra sensitivity. With an antenna element separation of λ/2 at the highest frequency the antenna width would be 4λ. Then at the highest frequency the beamwidth would be around 15° and the gain would be 23 dBi. At the lowest frequency the beamwidth and the gain would be around 45° and 13 dBi. With a frequency coverage of 6 – 18 GHz such an antenna would have an aperture width of 6.7 cm. A drawback with wideband phased arrays is their complexity and that they have a limited instantaneous field of view. Hence precise synchronization between the platforms is required in order to focus the antennas towards the threat under investigation. This does however require some knowledge of the position of the threat, something which may not be available.

B. Two antennas

0 20 40 60 80 100 120 140 160 180 200-90

-80

-70

[dB

m]

(a) Signal power [dBm] (threshold=-77 dBm)

0 20 40 60 80 100 120 140 160 180 200-90

-80

-70

[dB

m]

(b) Noise power [dBm] (threshold=-77 dBm)

0 20 40 60 80 100 120 140 160 180 200-90

-80

-70

[dB

m]

(c) Power of signal with noise [dBm] (threshold=-77 dBm)

Time [μs]

Figure 1. (a) Ideal received signal power in dBm, b) noise power in dBm, (c) power of signal and noise.

0 20 40 60 80 100 120 140 160 180 200

-90

-80

-70(a) 2-antenna mutual down-conversion (threshold=-79dBm)

0 20 40 60 80 100 120 140 160 180 200

-90

-80

-70(b) 2-antenna mutual down-conversion 16-point average (threshold=-88 dBm)

0 20 40 60 80 100 120 140 160 180 200

-90

-80

-70(c) 2-antenna mutual down-conversion 32-point average (threshold=-89 dBm)

Time [μs]

Figure 2. (a) 2-antenna signal mutual down-conversion, (b) and (c) down-conversion and 16- and 32-point averaging.

An alternative to a phased array is to use two separate equally directed antennas and two separate receivers. The noise and loss factor of a wideband receiver usually is of the order of 15 dB. Hence the internal noise in each receiver dominates the noise in the received signals. Now let us consider the signals from the same frequency channel for the two antennas. The signals received from a certain emitter will then be the same except for a constant phase difference and the two essentially independent receiver noises. In order to detect a signal one of the antenna signals is down-converted by the other and low-pass filtered.

Pulsed radar signals usually have no modulation, linear frequency modulation (LFM), binary phase shift keying (BPSK), poly-phase coding including digital variants of LFM, or more random complex frequency modulation like Costas codes. The pulse width and the bandwidth of typical modulated radar pulses can be expected to be 3 – 10 μs and 1 – 10 MHz, respectively. This means that LFM pulses have a frequency derivative of 0.1 – 3 MHz/μs.

In Figure 1. a set of -80 dBm pulses from a 20 MHz receiver frequency channel is shown, the receiver noise and the sum of the ideal signal and the noise. The three first signals have linear frequency modulation (LFM) with bandwidths of 5 MHz, 10 MHz and 10 MHz and pulsewidths of 3 μs, 5 μs and 10 μs. The fourth signal has a binary phase modulation with 31 subpulses with 150 ns length each. The two last pulses are short unmodulated pulses with pulsewidths of 1 μs and 0.5 μs. The noise floor reference is at -77 dBm.

In Figure 2. the down-conversion of one antenna signal with the other is shown with 1-, 16- and 32-point averaging. All -80 dBm signals can be easily seen above the noise background. The noise floors are at -79 dBm, -88 dBm and -89 dBm. This corresponds to processing gains of 2 dB, 11 dB and 12 dB. The time of arrival (TOA) of the pulses can be expected to be measured with an accuracy of around 100 ns for the case with 16-point averaging.

If the antenna separation is short the phase of the down-converted signal can be used for estimation of the angle of


arrival of the received signal. The major drawbacks with this approach are that it requires twice the hardware resources and space compared to a single channel approach and that the sensitivity will be dependent on the direction to the threat.

IV. USE OF SIGNAL PROCESSING

A. Short-time Fourier transform ( STFT) Signal power [dBm] after 16-point STFT (threshold=-89 dBm)

Time [μs]

Fre

quen

cy [

MH

z]

0 10 20 30 40 50 60 70 80 90 100

-10

-8

-6

-4

-2

0

2

4

6

8

Figure 3. Signal power in dBm after a 16-point STFT.

0 10 20 30 40 50 60 70 80 90 100-100

-90

-80

[dB

m]

(a) Some 16-point STFT frequency channels (threshold=-89 dBm)

0 10 20 30 40 50 60 70 80 90 100-100

-90

-80

[dB

m]

(b) Maximum of all 16-point STFT frequency channels (threshold=-89 dBm)

0 10 20 30 40 50 60 70 80 90 100-100

-90

-80

[dB

m]

(c) One 16-point STFT frequency channel (threshold=-89 dBm)

Time [μs]

Figure 4. (a) Some 16-point STFT frequency channels, (b) maximum of all frequency channels, (c) one of the frequency channels.

Typical threat LFM pulses have a frequency derivative of 0.1 – 3 MHz/μs. Hence a receiver could use a short-time Fourier transform (STFT) with a frequency resolution of around 1 MHz to create a time-frequency (TF) representation of the received signal. The next step could then be to search for patterns in this TF picture with an energy above a suitable threshold. In Figure 3. and Figure 4. a 16-point STFT of the noisy -80 dBm signal in Figure 1. is shown. Here we can clearly see the time-dependent instantaneous frequencies for the three LFM pulses. The BPSK pulse has an instantaneous spread of its power and the short unmodulated pulses are concentrated in frequency. The noise floor is at -89 dBm which

corresponds to a processing gain of 12 dB. The time of arrival (TOA) of the pulses can be measured with an accuracy of around 100 ns.

B. Signal squaring Power of squared signal [dBm] after 16-point STFT (threshold=-86 dBm)

Time [μs]

Fre

quen

cy [

MH

z]

0 10 20 30 40 50 60 70 80 90 100

-10

-8

-6

-4

-2

0

2

4

6

8

Figure 5. 16-point STFT of the square of the received noisy signal.

0 10 20 30 40 50 60 70 80 90 100

-90

-80

[1dB

m]

(a) Some channels of 16-point STFT of squared signal (threshold=-86 dBm)

0 10 20 30 40 50 60 70 80 90 100

-90

-80

[dB

m]

(b) Maximum of all channels of 16-point STFT of squared signal (threshold=-86 dBm)

0 10 20 30 40 50 60 70 80 90 100

-90

-80

[dB

m]

(c) One channel of 16-point STFT of squared signal (threshold=-86 dBm)

Time [μs]

Figure 6. (a) Some channels of 16-point STFT of squared signal, (b) maximum of all channels, (c) one of the channels.

Let us now consider a BPSK signal in a 20 MHz frequency channel. By squaring the signal we obtain an unmodulated narrowband signal with twice the original frequency. A 16-point short-time Fourier transform (STFT) for the square of the noise signal in Figure 1. is shown in Figure 5. A problem is that a peak is obtained at zero frequency due to the squared noise. This could be mitigated by instead multiplying the signal with a copy of the signal which is delayed one or two samples in order to decorrelate the product of the noise contributions. This does however require temporally uncorrelated noise. In the time-frequency picture shown in Figure 5. and in Figure 6. the beginning and end of each pulse is easily seen. The LFM pulses have doubled frequency ridges due to frequency folding of the doubled frequency. The BPSK pulse has a frequency ridge with constant frequency. Measurement of the carrier


frequency will be ambiguous in this case. The noise floor is at -86 dBm which corresponds to a processing gain of 9 dB.

C. Correlation with a delayed signal Costas codes can be seen as LFM pulses where subpulses

have been randomized in time. In order to detect Costas coded pulses we have to use more general methods. Radar pulses are often transmitted as a train of coherent equal pulses. In such a case one can correlate the received signal with a delayed copy of the signal. There will then be a pronounced peak for a delay that is equal to the pulse repetition interval (PRI). With an integration time of 1 μs there will be a processing gain of around 10 dB.

D. Matched filter method

0 20 40 60 80 100 120

-100

-90

-80

[dB

m]

(a) Signal + noise correlated with first LFM signal (threshold=-96 dBm)

0 20 40 60 80 100 120

-100

-90

-80

[dB

m]

(b) Signal + noise correlated with second LFM signal (threshold=-100 dBm)

0 20 40 60 80 100 120

-100

-90

-80

[dB

m]

(c) Signal + noise correlated with third LFM signal (threshold=-100 dBm)

Time [μs]

Figure 7. Correlation of the noisy received signal with ideal templates of the first (a), second (b) and the third (c) LFM pulses.

In a typical scenario at least one of the ESM platforms is illuminated by the radar main beam. Then the radar waveform can be measured at a high signal to noise ratio (SNR) and the parameters of the received pulses can be sent to the cooperating ESM platforms. These parameters can be used to compute pulse templates. The noisy received signal at the cooperating ESM platforms can then be correlated with these templates and obtain a large processing gain. This of course requires that the pulse waveform does not change. In Figure 7. the received noisy -80 dBm signal in Figure 1. is correlated with noise-free copies of each of the three LFM pulses. The time of arrival (TOA) of the pulses here can be measured with an accuracy of around 25 ns.

E. Video filtering If the signal to noise ratio (SNR) in the 20 MHz frequency

channel is larger than 0 dB then low-pass filtering can increase the sensitivity. In Figure 8. 16-, 32- and 64-point averages of the instantaneous power of the received noisy signal from a 20 MHz frequency channel from Figure 1. are shown. The five pulses are all easily seen.

The noise floors are at -83.5 dBm, -84.5 dBm and -85 dBm corresponding to processing gains of 6.5 dB, 7.5 dB and 8 dB.

If a pulse is detected the corresponding samples can be stored for a while in a short-term memory.

If a pulse template is available within a few seconds then important signal parameters can be measured with better accuracy using cross correlation of the signal and the pulse template.

0 20 40 60 80 100 120 140 160 180 200-86-84-82-80-78-76

[dB

m]

(a) 16-point average of instantaneous power (threshold=-83.5 dBm)

0 20 40 60 80 100 120 140 160 180 200-86-84-82-80-78-76

[dB

m]

(b) 32-point average of instantaneous power (threshold=-84.5 dBm)

0 20 40 60 80 100 120 140 160 180 200-86-84-82-80-78-76

[dB

m]

(c) 64-point average of instantaneous power (threshold=-85 dBm)

Time [μs]

Figure 8. 16-, 32- and 64-point averages of the instantaneous power.

V. SIGNAL MEASUREMENTS

If a pulse is detected by using some method then the corresponding signal samples can be stored in a pulse memory. Radars typically send trains of similar pulses. One can then correlate such a set of pulses with each other. The pulses can then be aligned in time and phase. By averaging between such a train of aligned pulses we can obtain a pulse template with reduced noise background. By correlating the original pulses with the template each pulse is replaced by a narrow pulse-compressed peak. Then time of arrival (TOA) and phase can be determined with high accuracy. From the relative phases of the pulses in the train high accuracy estimates of the frequency of arrival (FOA) can be obtained. By exchanging TOA and FOA measurement data between platforms high accuracy estimates of the emitter positions can be obtained (see Wiley [2]).

VI. DISCUSSION

The optimal way to get high sensitivity and perform high accuracy measurements would probably be to use a wideband phased array antenna with a gain of 15 – 25 dB combined with a channelized digital receiver with a channel bandwidth of 10 – 20 MHz. The drawbacks with such a solution is high complexity, high cost and that all contributing antennas must be pointing towards the same area of interest at the same time.

Using 16-point short-time Fourier transform (STFT) for selected 20 MHz channels is quite a good alternative with much less complexity. The processing gain with such a solution is a factor of 16 which equals 12 dB if most of the instantaneous signal power falls into one STFT channel at a time. STFT gives a time-frequency (TF) representation of the signal which can be used to measure pulse characteristics.


Pulses with binary phase shift keying (BPSK) with short subpulses have a large instantaneous bandwidth and then STFT can be less advantageous although such a pulse can often be seen quite well anyway in the STFT channels. A signal processing complement can be to perform a 16-point STFT on a squared channel signal instead since the binary phase-shifts are erased by the squaring of the signal.

Often a pulse template can be obtained from a signal library or from a cooperating ESM platform. Then the received channel signal can be correlated with this pulse template and for each pulse of that type a narrow strong spike is obtained enabling high accuracy measurements of time of arrival (TOA) and frequency of arrival (FOA). For a 10 μs pulse and a 20 MHz channel we will have a processing gain of a factor 200 or 23 dB.

A complement to the more advanced methods could be to use 16-, 32- or 64-point averaging of the signal power in order to obtain a substantial processing gain of 6 – 8 dB. The advantage with such incoherent integration is the low complexity of this method.

VII. CONCLUSIONS

It would be very advantageous for modern fighter aircraft to have a capability of obtaining high accuracy estimates of the

position of important threat radars after a few seconds. This is possible by exchanging high accuracy measurements of the time of arrival and frequency of arrival of signals to cooperating ESM platforms. This typically requires that some of the ESM platforms have to have sensitivity to detect and measure the signals in the radar sidelobes. After examining a set of possible methods we can conclude that there are several methods that can give a sensitivity that in many important scenarios will allow measurement also in the radar sidelobes.

REFERENCES

[1] Bradford W Parkinson, James J Spilker (Ed), “Global positioning system: Theory and applications”, Volume I and II, Progress in astronautics and aeronautics, 1996.

[2] R. G. Wiley, “ELINT: The interception and analysis of radar signals”, Artech House, 2006.

[3] J. B. Y. Tsui, “Digital techniques for wideband receivers”, Second edition, SciTech Publishing, 2004.

[4] James B Y Tsui, “Special design topics in digital wideband receivers”, Artech House, 2009.

[5] P. E. Pace, “Detecting and classifying low probability of intercept radar”, Artech House, 2009.


New solution to enhance the security in Air Traffic Control

Enrico Anniballi*, Roberta Cardinali+

SESM s.c.a.r.l. Via Tiburtina 1238

Rome, Italy *[email protected], [email protected]

Abstract — Conventional civil Air Traffic Control (ATC) systems are able to detect targets and identify collaborative aircrafts in the air space but they don’t assure full coverage at low altitude, in presence of non cooperative targets (NCTs) and aircraft A/C with a low value of radar cross section (RCS). In the following a new architecture to address this problem is proposed. The new ATC architecture foresees the combined use of conventional system, such as Primary and Secondary Radar, ADSB, etc., and innovative systems such as a new enhanced PSR, passive and bistatic radar network. In the following the new system architecture will be described. The approach foresees a data fusion center and a decision support system as a valid support for the situation awareness. Starting from the single sensors, the overall performance of the new ATC system are showed by a first preliminary analysis of data fusion. This results show the advantage of the new system respect to the conventional one specially in absence of the PSR coverage where the current system don’t allows to address the radar blind zones problem.

Keywords: Air traffic control; Passive radar; PSR; radar blind zone;. data fusion.

I. INTRODUCTION

After the recent terroristic acts, e.g. the failed attempt in December 2009 on the flight Amsterdam-Detroit, the recent terroristic attempt in Moscow airport in January 2011 and the current international situation, the security concept became very important to protect the people [2], especially in the air transportation field, always been considered critical for this issues. As a consequence the awareness about the possibility of large-scale, terrorist offensive actions delivered against civil society by means of aircrafts (A/C), that can be used either for carrying out “kinetic attacks” (i.e. crashing planes into buildings, as it happened for the 9/11 attack against the World Trade Center and the Pentagon) or for delivering chemical and/or biological pollutants is increasing.

Conventional Air Traffic Control (ATC) systems for civil applications cover large airspace and are able to locate a vehicle inside the coverage area and identify (considering A/C as “friendly”) only cooperative A/C (i.e. those responding to the interrogations from the Secondary Surveillance Radar - SSR), but they are characterised by the following main drawbacks [3]:

1. they do not assure the full coverage, particularly at low altitudes, due to the presence of the so called radar blind zones

(i.e. regions where the radar coverage of the Primary Surveillance Radar – PSR - and of the SSR are almost ineffective);

2. they are not able to fully localize non-cooperative targets (NCTs), because the PSR only returns range and azimuth angle and without response by the airborne transponder no information is available concerning the A/C altitude;

3. they are not able to identify NCTs, that could be either hostile A/C approaching a forbidden area and/or somehow threatening the homeland security (and therefore deserving a counteraction by the responsible for homeland security) or friend A/C having a breakdown (and therefore requiring some kind of help);

4. they are unable to detect A/C characterised by a low value of radar cross section (RCS), because they are tailored for efficiently monitoring large A/C.

II. SECURITY CONCEPT

The presented problem is addressed by the Air Guidance and Surveillance 3D (ARGUS 3D) project1 [1]. The overall objective of ARGUS 3D project is to enhance the security of European citizens, as well as of strategic assets by contrasting, on large areas, unpredictable and unexpected terrorist threats that can be delivered by means of small and low-flying (manned or unmanned) A/C.

In order to achieve this general objective, ARGUS 3D project intends to carry out research and development activities aimed at improving the current ATC systems for civil applications, extending their coverage and making them able to detect, recognize and track non-cooperative targets (NCTs).

The scientific and technical objective of ARGUS 3D project is studying, designing and implementing an innovative, low-cost, multi-sensor, radar-based system for 3D air guidance and surveillance that integrates conventional surveillance systems currently used for civil applications and two classes of non-conventional radar systems:

1. Innovative PSR sensors, i.e. an enhanced PSR with monopulse estimation capability in the vertical plane and therefore able to return the altitude information for any

1 Description of work of EU-project ARGUS3D, seventh framework

programme, theme #10 security Grant agreement no.: 218041


Figure 1 – ARGUS 3D architecture

detected target; 2. networks of multi-operational, high-performing passive

radar sensors [4][5], which are a special form of radar receivers that detect and track objects by processing reflections from non-cooperative sources of illumination already available in the environment (e.g. commercial broadcast and communications signals).

3.bistatic radar sensors [6] for extending range that further allow to detect anomalies in the air space.

III. SYSTEM ARCHITECTURE

The innovation and the added value of ARGUS 3D consist of the integration among new and traditional systems. Indeed it can be seen as composed by two main blocks. The first is related to the traditional systems (PSR, SSR ADS-B, etc.), the second is the part related to the innovative surveillance system. These new systems, are composed by:

• a new PSR that exploiting the difference of the gain of the radar beams of conventional multi-beam 2D PSR, allows to obtain an estimation of the A/C altitude.

• Passive radar network, which is able to detect and track potential targets by using the transmitters of opportunity avoiding to emit pulses and to be localized by other sensors.

• Bistatic radar network, which is able to detect and track potential targets by using the emission of cooperative or non-cooperative primary radar.

Figure 1 shows in more detail the ARGUS 3D architecture. The new system hasn’t influence on the traditional system that continues to work in its traditional way without affecting the operation as it is at present. It can be considered as an added

value .It can exploit the information of traditional system trying to obtain more information about the observed environment. To achieve this objective all data, coming from new and traditional systems, after signal processing and an appropriate interface, enter in the consistency block.

Due to the variety and non-homogeneity of the different subsystems that compose ARGUS 3D, a new data fusion block is required in order to combine the data of new and traditional systems. After data fusion the data are sent to the decision support system (DSS). This module can take three different decision according to the current situation. If there is a cooperative target with a regular route, the data are classified as “normal situation” and any alarm is triggered. If there is a cooperative target but an anomalous situation is detected, the data are sent to an operator that analyzes the situation and decides, after any further checks, if there is an anomalous situation that needs to trigger the alarm. In the worst case, i.e. when there is a presence of non-cooperative target and therefore also an anomalous situation (e.g. the SSR doesn’t work), the alarm is trigger automatically. Even if the inputs of the DSS are the data coming from the various sensors (only consistent data) it is also supported by a dynamic database that takes into account all a priori information available about the situation under test (e.g. assigned routes, altitude, etc.). Moreover, a new display will be developed by ARGUS 3D project in order to allow to the final user a more complete situation awareness that exploits both traditional and innovative data and the possibility to simply intervene in case of alarm.


IV. ARGUS 3D FUNCTIONAL REQUIREMENTS AND PERFORMANCES STUDY

The Table 1 shows the operational requirements of the overall ARGUS 3D system in terms of desired performances.

Table 1 – ARGUS 3D desired performances

In the following some preliminary results concerning the overall ARGUS3D system performances will be showed. It is important to highlight that the performed analysis are based on some simplified hypothesis:

• The PSR performance are evaluated considering that the target is in the main lobe of the radar, while the real antenna pattern is not considered;

• The received antennas are omni-directional, even if in real applications it is important to have an antenna with a very low gain in the back lobe (the gain in the direction of the transmitter has to be as small as possible in order to reduce the interference due to direct signal from the transmitter);

• The positions of the receivers do not take into account strengths, described in the previous point, due to the antenna pattern;

• The analysis is limited to the evaluation of combined performance of PSR and passive radar network that exploits FM radio transmitter as transmitter of opportunity.

• The FM radio transmitter antenna is considered omni-directional. This hypothesis is acceptable for the azimuthal gain that is not realistic for elevation antenna gain (the radio transmitter normally cover only ground area).

The analysis is performed exploiting to different scenarios:

the first one is a very simple scenario with a single mountain pick in the area, while the second one consider a representation of the morphology of a portion of Italian territory around the city of Rome.

The first results have been obtained by considering the first simple scenario showed in Figure 2, in which the PSR is highlighted with red star and a the transmitter of opportunity is represented in green.

Figure 2 – First considered scenario for the performances analysis

The considered area is 200x200 Km large and it is characterized by the presence of a mountain. The position of the transmitter of opportunity is defined to be on top of the mountain as usually happens in real cases. By considering an isotropic antenna pattern for the transmitter, this also ensures that all passive sensors, that will be located in the area, receive the signal from the transmitter even if with different power.

In the following an analysis of the detection probability (Pd) in the considered area is showed, both in presence of only the PSR, and in presence of PSR and new passive radars.

Figure 3 – ARGUS 3D performances (Pd) by considering only PSR (RCS=1, altitude=1000m)

Figure 3 represents the probability of detection in the considered area in the presence of only PSR coverage. The Pd


has been obtained by considering a radar cross-section (RCS) of 1m2 at the altitude of 1000m. As showed in the figure, the presence of the mountain doesn’t allow to assure the radar coverage behind it and the performances decrease quickly from a distance grater than about 130Km from the PSR.

Figure 4 – Overall ARGUS 3D performances (Pd) with PSR and passive radars (RCS=1 m2, altitude=1000m)

Figure 4 shows the same scenario where also the passive radar has been included. The passive receivers are highlighted in magenta. As expected, the performance of the single receiver are better in the area near the transmitter due to the higher value o the signal to noise ratio (SNR). Comparing the Figure 4 with the previous one it is possible to see how the area beyond the mountain respect to PSR, not covered in Figure 3 it’s now covered with an high value of Pd. Instead the area on the right is characterized from high values of Pd in the area close to the receivers but with small value elsewhere.

Figure 5 – overall ARGUS 3D performances (Pd) with PSR and passive radars (RCS=10m2, altitude=1000m)

This problem could be solved by optimizing the receivers position respect to the received SNR. In this way in the area near the transmitters the receivers should have a higher distance respect to the more distant area. The blue area on the left-top of Figure 4 represent the physical occupation of the mountain: obviously in that area, the expected Pd is zero. Figure 5 shows the simulation results by considering the same

parameters of the previous ones, but with a RCS equal to 10m2. In this case the PSR covers a larger area with an high value of Pd. By analyzing the figure it is clear that less passive receivers are needed to have better performance. The effectiveness of PSR guarantees high performance that hide the effects of passive radar.

Figure 6 – overall ARGUS 3D performances (Pd) with PSR and passive radars (RCS=1 m2, altitude=10000m)

Figure 6 shows the overall ARGUS3D performance with a RCS equal to 1m2 but this time it is calculated by considering an altitude equal to 10000m. As it possible to see, the blind zone due to the mountain is not present for the high altitude. However the performances of the passive receivers are worse than Figure 4. This can be justified because the path transmitter-target-receivers is longer at high altitude and the received signal, and consequently the signal to noise ratio, is smaller. The analysis follows using the system in a realistic scenario, shown in Figure 7, that represents the area around Rome city in Italy. In this case, the area analyzed in more complex for the simultaneous presence of sea and mountain that limit the PSR performance and the possibility to locate Passive receivers.

Figure 7 – Second realistic scenario for the performances analysis

As expected, the presence of mountains creates blind zone for the PSR at low altitude: Figure 8 shows the detection


probability of primary radar for a target with a RCS of 10 m2

at 1000m of altitude. It is evident that beyond the mountain picks a low altitude flying vehicle is not detectable by PSR even if its distance is inferior to the maximum detectable range. Instead, the presence of sea in a wide area of the scenario from some point of view is positive because the PSR assures a wide coverage for the absence of physical obstacles, on the other hand the sea does not allow to install Passive Coherent Locator (PCL) receivers if necessary.

Figure 8 – ARGUS 3D performances (Pd) for realistic scenario by considering only PSR (RCS=10 m2, altitude=1000m)

In order to overcome this problem it is possible to introduce passive radar to fill the gaps of conventional ATM systems.

Figure 9 – Overall ARGUS 3D performances (Pd) for realistic scenario with PSR and passive radars (RCS=1o m2,

altitude=1000m) Figure 9 shows the performance of both PSR and passive

radar network. Particularly the yellow star represent the position of PSR, the green star the position of the transmitter of opportunity of PCL network and the pink stars the position of PCL receivers. It is evident that the presence of passive radar network allows to increase the performance of ATM system even in areas not covered by PSR. The blue areas that remain in the figure are zones where the mountain altitude is superior than 1000m and then it is not possible to evaluate the performance in that areas because the space is physically occupied by the mountain.

V. FUTURE WORKS

Future activities foreseen the optimization of the receivers position taking into account different aspects not considered in this analysis, as:

• the antenna pattern of transmitter and receivers, • the localization of PCL receivers only in the real

blind zone PSR in the interested area • More than one transmitter in the area. • To consider also the presence of different transmitter

of opportunity, as DVB-T, DAB, etc. • The effect of blindness in the baseline between

transmitter and receiver. Moreover, further analysis on the data accuracy will performed in order to evaluate the added value of the innovative architecture not only in term of coverage, but also in term of ARGUS 3D system achievable accuracy and resolution.

VI. CONCLUSION

In this paper, a new system concept to enhance the security of European citizens, as well as of strategic assets by contrasting, on large areas, unpredictable and unexpected terrorist threats that can be delivered by means of small and low-flying (manned or unmanned) A/C was presented. Using a combined way of conventional system, new 3D PSR and network of multi-operational, high-performing passive/bistatic radar sensors, ARGUS 3D intend to improve the current ATC systems for civil applications, extending their coverage and making them able to detect, recognise and track non-cooperative targets. Moreover, the presence of multiple sensors allows to increase also the accuracy of target position. This was proved both in a simple scenario and in a realistic scenario that takes into account the real morphology of Italian territory.

It has been showed as the overall performances depend by many factors. In particular the overall Pd decrease for small value of RCS and for high altitude, particularly in the areas covered only by passive radar. Basing on the performances to be achieved, the location of the passive receivers have to be decided by considering the received SNR. The performed analysis can be used as preliminary instrument for the localization of PCL receivers in under surveillance area.

The possibility to locate non-cooperative target in the PSR blind zone allows to increase the air-space and air traffic security and consequently the citizen security. The presented simulations show the first preliminary results of ARGUS 3D performances.

VII. REFERENCES

[1] Description of work of EU-project ARGUS3D, seventh framework programme, theme #10 security Grant agreement no.: 218041

[2] Understanding the Threat of “New Terrorism” (March 2007)


[3] M. I. Skolnik, Radar Handbook, 2nd ed. McGraw-Hill, 1990.

[4] Griffiths, H.D.; Baker, C.J., “Passive coherent location radar systems. Part 1: performance prediction” IEE Proc. Radar, Sonar and Navigation, Volume 152, No. 3, June 2005, pp.153-159

[5] A. Lauri, F. Colone, R. Cardinali, C. Bongioanni, P. Lombardo, “Analysis and emulation of FM radio signals for passive radar”, 2007 IEEE Aerospace Conference, Big Sky (MT), USA, 3-10 March 2007.

[6] N. J. Willis, Bistatic Radar, Artech House, 1991


hθ1 θn

Lσ

Sσ

hθ1 θn

hθ1 θn

Lσ

Sσ

Solving the Data-Link bottleneck for MPEG Location

Jean-François GRANDIN, Luc BOSSER Thales Systèmes Aéroportés

2 Avenue Gay Lussac 78851 Elancourt CEDEX FRANCE [email protected]

Dario BENVENUTI ELETRONICA

Via Tiburtina Valeria Km 13,700 - 00131 Roma, ITALIA

Göran TENGSTRAND, Viktor ANDERSSON, Peter HULTMAN

SAAB Saab EDS, SE-17588 Järfälla, Sweden

Börje ANDERSSON, Anders JOHANSSON FOI Swedish Defence Research Agency,

P.O. Box 1165, SE-581 11 Linköping, SWEDEN

Abstract— The aim of MPEG (Multi-Platforms Emitter Geolocation) is to use several measurements Angle Of Arrival (AOA) or Time Of Arrival/Frequency Of Arrival (TOA/FOA) from different Electronic Support Measures (ESM) platforms to determine an accurate emitter location. Sensors are geographically dispersed, and to combine the information that they acquire requires the information to be communicated to some location(s) where the data integration process occurs. The data link between the ESM platforms is expected to permit only very low data rates with a high latency. This means that the platforms can only exchange very restricted amounts of data. A potentially optimal way to compress the information when the measurements are noisy is to extract from the measurements an exhaustive statistic that summarizes all the information content at the local ESM. How to provide an exhaustive statistics for practical measurements system in the case of information fusion is presented. It is done either on Information Matrix form or based on a Taylor series expansion of the measurement in time or space.

Keywords- Multi Platforms Emitter Geolocation, Time Difference of Arrival, Angle Of Arrival, Data Link.

I. INTRODUCTION

The aim of MPEG is to use several measurements from different ESM platforms to determine an accurate emitter location. Sensors are geographically dispersed, and to combine the information that they acquire requires the information to be communicated to some location(s) where the data integration process occurs.

The data link between the ESM platforms is expected to permit only very low data rates with a high latency. This means that the platforms can only exchange very restricted amounts of data. To obtain the best accuracy the platforms would need to share all measurements that are performed for an emitter and then use this data to calculate emitter location just as can be done on a single platform. This would however result in too high data rates for the data link. Another solution is that each platform uses all of its own measurements to locally calculate emitter location and then shares this information or a summary of measurements instead of each separate measurement.

. A potentially optimal way to compress the information when the measurements are noisy is to extract from the measurements an exhaustive statistic that summarizes all the information content at the local ESM. How to provide

an exhaustive statistics for practical measurements system in the case of information fusion is presented. It is done either on Information Matrix form or based on a Taylor series expansion of the measurement in time or space.

II. MPEG LOCATION

A. SP-AOA location basics Intercept AOA measurement over time by a moving

platform is often used for triangulation of the emitter. Considering the following geometry:

Figure 1. MAOA Interception Geometry

Under the hypothesis of a regular measurement sampling, the CRLB calculation gives the following EEP values:

( ) ( )2222

sin12

sin12

θθ σθθ

σσθθ

σΔ+Δ

=Δ−Δ

=NLh

NLh

sL

Figure 2. EEP caracteristics

In this equation N is the number of equivalent uncorrelated measurements. N is also called the number of effective

The authors acknowledge the European Defence Agency, the French DGA, the Swedish FMV and the Italian MoD, which have contributed to the funding of this study under EDA contract n°B-0055-IAP2-ERG.


θδθδΔ

≈RR

θΔ

For time limited illumination (critical target)

VTRR δθδ 2≈

measurements. It has to be limited by the correlation radius of the error. For (h>>L), this expression can be further simplified to:

θθ σσσσ hNL

hhN RA

12,1 ==

Which in full lateral ( °≈ 90θ ) leads to:

θσσ θ

Δ=

NRR 12

If the radar target is at 50 km distance, and a quite advanced interferometer is used (0.3º accuracy) the best location accuracy can only be obtained flying a long baseline (e.g more than 150 seconds for an aircraft at 300m/s speed) as a good angular aperture between the measurements is required.

B. M- AOA Location Basics The MP-AOA aims at delivering an instantaneous and

accurate target location by using several Lines of Bearing coming from N different sensors located at several positions remote from each other. Theoretically, MP-AOA can achieve accurate location as soon as the target’s beam has illuminated all the sensors during one scanning excursion.

Multiple platforms AOA offer a direct improvement in time-line, and the location accuracy will then depend on the measurement accuracy of each of the contributors and their geometrical positions. The angular aperture and distances of the platforms in the network will provide a result without needing a long integration time. This is the major advantage of MPEG over SPEG.

Figure 3. Advantage of MPEG over SPEG

If the target manage its illumination time, this will defeat SPEG method.

III. DATA LINK REDUCTION PRINCIPLE

Sensors are geographically dispersed, and to combine the information that they acquire requires the information to be communicated to some location(s) where the data integration process occurs. There are 2 possible ways to proceed:

• First possibility consists in communicating exhaustively each AOA measurement that has been carried out.

Figure 4. Measurements Fusion

• Second possibility consists in making an angular tracking of each target, performed by each ESM sensor. In this case, the state of the track is regularly communicated.

Figure 5. Tracks or Information Fusion

Due to the constraints in the communications bandwidth, in a practical multi-sensor tracking network, the sensor data processors communicate only a subset of the data available from each sensor, usually in the form of tracks but alternatively in the form of information summaries.

This can also be applied to MP-TDOA location according the following diagram:

Figure 6. AOA (Angle Of Arrival) and HOA (Hyperbole Of Arrival)


Local Angular Tracking (2D)

3D Tracks initiation (Deghosting)

3D Tracking

2D tracks non assigned

3D tracks 3D Tracks

Selected 2D tracks New 3D

Tracks

Local ESM perimeter

IV. ARCHITECTURE CHOICE

Architectures and Algorithms for Network Sensor Fusion are presented in famous tutorial books like[1][2]. Paper [3] gives an overview of different architectures from centralized, hierarchical to distributed.

A central approach non only presents a saturation risks with all the measurements transmitted but turns out " inextricable from the point of view of the association " while a local tracking establishes a reasonable approach reducing considerably the difficulties of association with a loss of almost nonexistant intrinsic performance. The general Hybrid-architecture is the following:

Figure 7. Hybrid Architecture for ESMs passive tracking

In this architecture the 2D or 3D tracks are initialized at the level of the passive sensors.

The association of the passive data (DOA or 2D tracks) is treated(handled) at the level of the passive sensor, whether it is the association of the DOA in the 2D angular tracks or the formation of the 3D tracks from a treatment of association of the passive 2D tracks.

The central tracker (3D tracking) has for objective to maintain the estimation and the prediction of the 3D tracks by taking into account the knowledge of the dynamics of targets. This central tracker realizes no treatment of association. In the case of fixed target this estimation and prediction is the most simple. The updated 3D tracks must be sent back to the local ESM to increase the quality of the local ESM association. These 3D tracks must be also feedback towards the process of initialization of the 3D tracks to avoid the creation of redundant 3D tracks. The process of initialisation of the 3D tracks realizes the deghosting.

This architecture is distributed on all the observers.

V. TRACK AND INFORMATION FUSION COMPARED

A. Track Fusion Track fusion is the best popular approach and its limitations

when used with feedback have been explained and solved in different papers [4][5][6][7]. Above all, Track fusion is not well suited for passive sensors where the local unobservability problem occurs.

In track fusion sensor-level tracks are combined to form global-level tracks that are based on data from all sensors.

If ( )Kk PX , is the information (state, covariance) given by sensor k. The track fusion without feedback and with uncorrelated measurements is given by:

=

−−

=

−=N

kkk

N

kkF XPPX

1

11

1

1

and

1

1

1−

=

−=N

kkF PP

(Equ.1)

In this equation KP has to be invertible which is not the case in a passive system above all at the beginning of the measurements. Even if KP is invertible, if it is ill-conditioned numerical errors appear and are accumulated during successive integration of measurements. When using track fusion, there is the implicit requirement that the tracks and their covariance matrices exist and are invertible. This is always the case in radar and the track fusion has been designed at the beginning for this type of sensor.

As integrating all the measurements from all sensors global tracks are more accurate that sensor-level tracks. That’s why it is relevant to feed back global tracks to the sensor-level trackers to reduce the data association errors.

The global tracks, however, are cross correlated with the sensor-level tracks. This track to track correlation should be taken into account in algorithm design. The paper [4] presents algorithm architectures and methods for dealing with the cross-correlations of the tracks in track fusion for feeding back the global level tracks. All these algorithms use a “decorrelator” in order to remove the cross-correlation between the sensor level tracks and the global level tracks.

But there is an alternative to this scheme which consists to exchange Information and not tracks.

B. Information Fusion In Information fusion sensor-level information summaries

are combined to form global-level tracks that are based on data from all sensors.

In linear or pseudo linear cases measurements and state are related by the following type of linear equation:

XHZ nn =

The optimal solution that minimizes the mean quadratic error is given by:

( ) ( ) nTnn

Tnn ZHHHX 1ˆ −=

An iterative calculation can be used by applying the recursive process on the coefficients of matrix

( ) nTnn HHInf = and n

Tn ZH .

It’s interesting to notice that ( ) nTnn HHInf = is the

expression of the Information matrix, whose inversion can provide the Cramer Rao Boundary in the Gaussian case.


If ( )KK JI , is the information given by sensor k.

kTkk HHI =

kTkk ZHJ =

( ) FFF JIX 1ˆ −= with

=

=N

kkF II

1

and =

=N

kkF JJ

1

In the general case, in passive systems information matrices exist but are often ill-conditioned and even non invertible. Fortunately the combined information matrix becomes invertible with good conditioning until the passive systems are well separated in space.

To avoid the decorrelation phase the purpose is to not communicate tracks but statistical summaries which are independent each other. The processing is according the following diagrams:

Figure 8. Information Fusion Processing

In the processing the global level tracks are feedback but just for association.

For non-linear least squares systems similar arguments shows that the solution should be generalized as in VI.

C. Comparison As said previously, there are two opposite ways to proceed

when we deal with multi-platform case:

• First one is sharing exhaustively every line of bearing calculated after each target beam interception,

• Second one is sharing summarized relevant information about each target: average line of bearing an associated covariance, for instance.

However, each solution presents serious drawbacks:

• In the first case, we communicate a huge amount of redundant information, without any relevant filtering or compression. As a consequence, this method requires high data rate communication capabilities.

• In the other case, some interesting information can be lost.

The optimal solution consists in communicating some compressed but relevant information that offers the best trade off between the following competing requirements:

• Optimal geo-location performances (obtained when all the measurements are available).

• Minimal communication rate between platforms.

The question now is “what summary to transmit?”. This question is fundamental either for track fusion or for information fusion. It has been addressed for information fusion in [8] and for track fusion in [9].

How to provide an exhaustive statistics for practical measurements system in the case of information fusion will be presented in paragraph xx. It will be done either on Information Matrix form or based on a Taylor series expansion of the measurement in time or space.

VI. DETAILS OF SUMMARIZATION METHOD

A. Estimators decomposition - Principle The optimal estimation is obtained by processing the entire

amount of available measurements collected by every platform. We can make a decomposition of this optimal estimation by separate processing of measurements collected by each observer, before sharing results coming from these different observers. The overall amount of calculation is parallelized between observers, which finally share a limited amount of data. This data is composed of intermediate results that participate to the final geo-location estimation.

1) Linear caseIn that case, the calculation does not require any iteration

and an additive decomposition can be easily obtained. Each observer calculates partial estimations using his own data, before sharing these intermediate results with the other observers.

Only a limited number of coefficients have to be exchanged via the communication network.

2) Non Linear caseThe issue of measurements compression consists in

representing accurately numerous observations by using a limited number of coefficients, without any significant loss of information.

Generally speaking, Taylor expansion can offer an interesting solution to this problem in some case, as well as Fourier Transform, for instance.

We can assess compression efficiency by comparing:

• the actual observations

• the observations that are re-estimated from the limited number of coefficients that have been calculated according to the chosen compression modeling.


θ

d

vr

vθ

ESM 1

ESM 0 θ

d

vr

vθ

ESM 1

ESM 0

In order to choose a relevant modeling for compressing angular measurements, several parameters must be taken into account (geometrical configurations of acquisitions, sensors characterization, …).

The method is based on Taylor series Expansion of the measurement equation:

( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )( ) ( ) 1

12

!1!....

!2!1+

+

−+

+−++−′′

+−′

+= nn

nn

axn

faxn

afaxafaxafafxf ξ

For a function of several variables (3 here) it becomes:

( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) .......,,,,,,,,,, +−∂

∂+−∂

∂+−∂

∂+====

===

=== cz

zzyxfby

yzyxfax

xzyxfcbafzyxf

czbyax

czbyax

czbyax

The validation of regression validity considers values of higher order terms.

If the relative motion is MRU Expansion in time is sufficient.

3) Application to TDOA and AOA

According to the following diagram:

The measurement equation is:

( ) ( ) ( )( ) ( )c

trtrtrc

t Δ=−=Δ 011τ

State of the source at t=0 : ( )vrvr ,,, θθRelative motion is MRU:

( ) ( )( )trtvt /.sin 1 θθθ −+=

( ) ( ) ( )22 .. tvtvrrtr θ++=

( ) ( ) ( ) ( )( ) ( ) ( )( ) 2/11

21

21 1cos2 ttrttrddtrtr δθ +=−+=

( ) ( ) ( )( )( )tr

ttrddt 21

21 cos2 θδ −= <<1

( ) ( ) ( )( )( ) ( ) ( ) ( )+−≈+−=Δ82

1111 22/1

1tttr

cttr

ct δδδτ

This gives

( ) ( )( ) ( )( )( )

( )( )( ) ( )tcr

dtcr

tdtcr

tdc

tdt 3

4

2

322

1 82cos

2sincos +−−=Δ θθθτ

Where it is possible to neglect higher order terms

( ) 0=∂∂= tttr

v θθ ( ) 0=∂

∂= ttrt

vr

( ) ( )

( ) 4

4

3

3

2

2

01

83cos2sin

2

cos2sin2sinsin

crdvrv

crd

vvrcr

dr

vc

dtt t

−++

−+=Δ∂∂

=

θθθ

θθθθθθτ

Where it is possible to neglect higher order terms. This leads to:

( ) ( ) ( ) ( ) ( )trrttt

t t .,,.0 110111 θβθατττ +=Δ∂∂+Δ=Δ =

( ) ( ) ( )( )

( )

( ) ( )−−−=

−=

+=Δ

θθθθθθθβ

θθθα

θβθατ

cos2sin2sinsin,

2sincos,

where

.,,

21

2

1

111

vvrr

dr

vrr

dr

trrcdt

AOA case is when d/r <<<< 1. This leads to:

( ) ( ) ( )( )( )

( ) −=

=

+=Δ

rvr

r

trrcdt

θθθβ

θθα

θβθατ

sin,

cos, where

.,,

1

1

111

4) Implementation A possible implementation of the method is the following:

• Robust regression on Measurements.

• Analysis of Regressor variances to validate regression

o If not validated: Divise the data and iterate.

o Once validated: Transmit regression coefficients:

First Bearing (denoised)

Last Bearing (denoised)

Number of estimated independant bearings

Bearing rates (not fully required for fixed emitters)


The full MP-AOA location distribution is represented according the following figure:

Figure 9. Location Distribution Principle

VII. ILLUSTRATIVE SIMULATION

The following example considers a patrol of two aircrafts targeting an hostile aircraft which is emitted.

In the centralized case Instantaneous triangulation are filtered.

Figure 10. Location with centralized method.

Figure 11. Location with information fusion method

Due to the information fusion method the DATA LINK rate is reduced.

VIII. CONCLUSION

By 2010-2020 the infosphere will be very different of what we know today. There will be manned and unmanned platforms in air/sea/land all connected via data links. The requirements on amount of data to exchange will increase by several orders of magnitude. It will be of first importance to minimize data transmission while preserving important data. The method presented here offers a very attractive alternative to solve the bottleneck of data transmission.

ACKNOWLEDGMENT

The authors acknowledge the European Defence Agency, the French DGA, the Swedish FMV and the Italian MoD, which have contributed to the funding of this study under EDA contract n°B-0055-IAP2-ERG.

REFERENCES

[1] Y. Bar-Shalom, « Multitarget-Multisensor tracking : Principles and techniques », 3ième édition, 1995.

[2] S. Blackmann, R. Popoli, « Design and analysis of modern tracking systems », Artech House, 1999.

[3] C.Y. Chong, S. Mori, W.H. Barker and K.C. Chang, ” Architectures and Algorithms for Track Association and Fusion ”, in IEEE AES Systems Magazine, January 2000

[4] Olivier E.Drummond “Track Fusion with Feedback” SPIE vol 2759 [5] Y. Bar-Shalom, “On the Track-to-Track Correlation Problem”, in IEEE

Transactions on Automatic Control, vol. ac-26, n°2, April 1981 [6] X. Rong Li, Y. Zhu and C. Han, “Unified Optimal Linear Estimation

Fusion – Part I : Unified Models and Fusion Rules”, in Proceedings of 2000 International Conference on Information Fusion, Paris, France, July 2000

[7] O. Drummond, “A Hybrid Sensor Fusion Algorithm Architecture and Tracklet”, in Proceedings of SPIE, vol. 3163, 1997

[8] JF Grandin, L Ratton « Procédé et système de pistage et de suivi d’émetteurs » patent number : N° 08 01715. date 28.03.2008 Extended to European level : Date 12.03.2009 N°09154963.4

[9] Xuezhi Wang, Rob Evans, Jonathan Legg “Distributed Sensor Fusion with Network Constraints” SPIE vol 5429 2004


Parasitic Doppler effect in passive location

Dario Benvenuti Research and Advanced System Design

Elettronica SpA Via Tiburtina Valeria, km 13.7, Rome, Italy

[email protected]

Abstract— Large base interferometry is a fast and accurate technique for passive emitter location. Experiments both in direct and inverse configuration have been executed and in this paper some results are presented in which the main sources of errors in Doppler measurements are analyzed. The observer attitude measurements need high accuracy, because it can lead to large estimation errors. Vibrations can be a problem in case of wings mounted antennas. The Doppler induced by the scanning antenna phase center displacement has been analysed as a possible source of error also in the inverse configuration where much of the disturbances are eliminated.

Keywords--passive location; large base interferometry; parasitic doppler

Passive location of surface emitters by an airborne observer through the large base interferometry is much faster and more accurate than standard triangulation and bearing-only tracking. The principle is that the frequency difference of arrival (FDOA) is related to the apparent angular velocity of the emitter with respect to the observer, θ

( )θλθ

sinBFDOA= (1)

Moreover the time difference of arrival (TDOA) is related to the direction of arrival (DOA), θ :

⋅= −

BcTDOA1cosθ (2)

so with two differential measurements, FDOA and TDOA location can be calculated:

( )θαθ

−= sinVR (3)

where α is the angle between the velocity vector and the interferometer [1, 2].

The error analysis shows a very good behaviour of this approach for emitter that are not pointed by the observer, and that are not aligned with the interferometer antennas. In fact the

emitter on the observer velocity direction show a null angular rate which in turn does not allow the range estimation, while the angular measurement is bad conditioned around the direction of the interferometer antennas [2].

I. EXPERIMENTAL TRIALS

A series of field test have been conducted by ELT in the framework of a research contract with Italian MoD, with the collaboration of Aeronautica Militare Italiana (AMI). The first campaign was performed in 2006 with a Passive Location Demonstrator (PLD) installed on a helicopter [2]. The constrained baseline along with the low velocity of the helicopter allows a reduced performance location.

A second field test has been recently carried on with the collaboration of an aircraft. These trials were in the inverse configuration, that is the PLD was standing on the ground and the aircraft was emitting with his own radar. That is not a problem because the relative motion is the physical entity that is to be measured, provided the aircraft route and velocity is known.

Figure 1. Localization result of a trial from [2].

The first campaign results have already been published [2] showing the average result of about 10%, in this paper a bad trial with a larger error will be analysed.


The aircraft campaign yielded better results as it can be expected given the higher velocity, a larger base and better conditions due to inverse configuration. Some trials yield a final location accuracy on the order of 2-3%, however some trials have still an error higher than 5% and an hypothesis about the error will be presented.

-16 -14 -12 -10 -8 -6 -4 -2 0 2-6

-4

-2

0

2

4

6

X [km]

Y [

km]

True trajectoryMeasurementsFilteringEmitting positions

Figure 2. Localization result of a trial of the aircraft campaign.

II. SOURCE OF ERRORS IN HELICOPTER TRIALS

The main source of error in some of these trials has been due to the helicopter attitude: in fact though the trajectory was rectilinear and uniform some perturbations during the flight are unavoidable and the attitude sensor may be not as accurate as needed. Equation (3) with the contribution of own platform rotation ωP becomes:

( )θαωθ

−−

= sinP

VR (4)

where the contribution of ωP to the error is quite important [2].

Figure 3. Trial 1: platform own motion compared to LOS variation.

In figure 3 the measured yaw rate and the total estimated ωare reported along with their difference and the expected line of

sight (LOS) variation. It is evident that the not perfect cancellation of the own platform rotation is comparable to the quantity to be measured, and in fact in this trial the localization error has been very large (see figure 4)

0 5 10 15 20 25 30 350

10

20

30

40

50

60

70

80

90

100

Relative Time [s] [

%]

Localization Trial 17/07/2006, 09:31

Percentage Range Error

Figure 4. Trial 1: localization error.

Another possible source of error in operative conditions is the antenna vibrations, especially for wing mounted antennas. In these trials that was not the case because the antenna structure was quite robust and rigid. Simulations have been performed with different amount of vibrations and the results are presented.

In Figure 5 a typical vibration spectrum is reported: the z-component displacement is plotted as a function of frequency and the corresponding velocity is evaluated. The main mode is about 5 Hz with maximum displacement 30 cm, this should be conservative for a combat aircraft; the higher modes present less displacement but, due to higher frequency, still have a considerable effect on velocity.

0 5 10 15 20 25 30 35 40 45 500

0.1

0.2

0.3

0.4

ΔZ [

m]

0 5 10 15 20 25 30 35 40 45 500

5

10

15

20

f [Hz]

V [

m/s

]

Figure 5. Vibrations spectrum: displacement (in blu) and velocity (in red)

The relative localization error due to vibrations is reported in figure 6. In the left hand vibrations have been imposed as a


common mode on the two wings, while on the right they are partially uncorrelated.

0 20 40 600

0.2

0.4

0.6

0.8

1

f [Hz]

Rel

ativ

e er

ror

Common mode vibrations

0 20 40 600

0.2

0.4

0.6

0.8

1

f [Hz]

Rel

ativ

e er

ror

Partially uncorrelated vibrations

Figure 6. Error induced by wings vibrations: (left) common mode vibration; (right) partially uncorrelated vibrations

The effect of vibrations as a common mode on the two antennas is just detectable and, being short time correlated, can be averaged away. However as soon as a mismatched vibration is set on the two wings a much higher effect arises which severely penalizes the wing mount configuration; thus it is a much better choice, also for the DOA estimation constraints, to install the interferometer antennas on the aircraft fuselage than on the wings.

III. ERRORS IN INVERSE CONFIGURATION TRIALS

In the inverse configuration the effect of platform attitude variations is much less important and vibrations are not present, by the way the antenna configuration is longitudinal (fuselage mount) thus vibrations should be negligible also in real conditions. Let's consider the following model (see figure 7) for the FDOA calculation, in which the origin of the coordinate system coincides with the center of PLD, B is the antennas distance along the x-axis, P(x0, y0) is the position of the aircraft (i.e. the emitting antenna) at time t0, and V is the aircraft velocity, assumed parallel to the x-axis.

Figure 7. Geometrical model for FDOA evaluation.

The FDOA is the time derivative of the following phase difference:

( ) ( ) ( )

( ) ( )−+−+−

−−+−−=Δ

20

20

20

20

2/

2/2

tryVtBx

tryVtBxt

ω

ωπλφ (5)

where the attitude variations are summarized in the ω (yaw rate) and r is the distance of the emitting antenna from the aircraft center of mass.

In a rectilinear trajectory heading variations of as much as 1°/s have been observed, causing an error in the FDOA less than 2 %, with the geometry depicted, where x0 = 20 km, y0 = 1 km.

In some trials a much larger error has been observed, like in figure 8, where the true trajectory is reported in black solid line along with the estimated positions in red circles. Here each measurement is achieved on a burst of 128 pulses.

-25 -20 -15 -10 -5 0 5 10-10

-5

0

5

10

X [km]

Y [

km]

True trajectoryMeasurements

Figure 8. Trial a/c 14: localization result.

A possible cause of error is due to the antenna phase center displacement during the scansion: if the phase center is not coincident with the rotation center a parasitic Doppler effect arises. The phase center displacement can exist also for phased array antennas, if the scan phase differences are not symmetric with respect to the geometric center of the array.

Figure 9. Antenna phase center displacement.

In figure 9 a) a simple 2-element array is depicted with broadside configuration: the phase center is the geometric center. When the array is scanning, if the phases are symmetric the phase center remains fixed. Often phases of the array elements are all the same sign, giving rise to a displacement of the phase center, this is particularly evident with true time delay phased array. This effect is meaningless for the radar itself, and can be however corrected for, while it has an effect on the FDOA estimation.


To evaluate this effect (5) can be used again where now r is the antenna half width and ω is the scan rate; for reasonable values of the parameters (r = 0.5 m, ω = 2 rad/s) this yields an error of 8% with the same geometrical setup as above.

This value certainly does not explain the large errors of figure 8, but it must be noted that a phased array antenna can point the beam in a pulse to pulse way, giving rise to a much higher ω. That is the case of the radar used in the experimentation, as it can be seen from the amplitude pattern reported in figure 10: it shows the main beam and a replica about 4 dB lower which is probably due to a second beam position.

Unfortunately the exact working of the radar and the details of its construction are not accessible to our analysis, but the performed evaluation can be useful to interpret some unexplainable results and in general to give a warning on the subtleties that lay behind these kind of techniques.

0 0.01 0.02 0.03 0.04 0.05 0.06-42

-41

-40

-39

-38

-37

-36

-35

-34

-33

-32

Time [s]

Am

p [d

B]

Figure 10. Pulse to pulse beam pointing of radar used in a/c trials.

IV. CONCLUSIONS

In this paper results of experimental trials of passive localization through phase difference rate estimation are presented. Several sources of error have been analyzed and taken into account.

The first experimental campaign was performed by the use of a helicopter and thus presented critical features like slow platform velocity, perturbed straight flight and not perfect attitude compensation.

The second campaign was performed in more controlled conditions (inverse configuration) and with the use of a combat aircraft; the results were surely better than the first campaign, however still in some trials the error was surprisingly high.

An hypothesis on the aircraft radar has been done: the phase center is conjectured to move with beam scanning, and the effect has been evaluated with reasonable parameters values showing a not negligible effect on localization.

Though we are not sure about the real working of the aircraft radar the performed analysis can be useful to have an insight on the localization techniques based on phase estimation.

ACKNOWLEDGMENT

I wish to thank my colleagues involved in the passive localization topic, in particular Antonio Zaccaron for the clarity of his suggestions, and my head Daniela Pistoia for the support.

REFERENCES

[1] X.-P. Deng, Z. Liu, W. -L. Jiang, Y. -Y. Zhou and Y. -W. Xu, “Passive location method and accuracy analysis with phase difference rate measurements”, IEE Proc. Radar, Sonar Navig., Vol. 148, No. 5, October 2001

[2] G. Severino, A. Zaccaron and R. Ardoino, “Performance of a Doppler based direct passive location technique”, ESAV 2008, Capri, 3-5 September 2008

[3] K. Becker, “An efficient method of passive emitter location”, IEEE Trans. on AES, vol. 28, No. 4, October 1992.


An in-air passive acoustic surveillance system for air traffic control

GUARDIAN project

Vincenzo Quaranta and Salvatore Ameduri CIRA, Italian Aerospace Research Centre

Vibration & Acoustics Lab. Via Maiorise, Capua (CE), Italy

E-mail: [email protected]

Domenico Donisi and Marco Bonamente D’Appolonia S.p.A.

Largo Carlo Salinari, 18/19 – 00142 Rome – Italy Headquarters: Via San Nazaro 19 – 16145 Genova – Italy

E-mail: [email protected]

Abstract—The air traffic control inside ATZ (Aerodrome Traffic Zone) is a key activity for airport management services to meet increased security and a low environmental impact on air transport systems. The GUARDIAN system aims to the development of an acoustic system as support for airplane traffic control in aerodrome zone.

Keywords: traffic management systems, aircraft detection and location, acoustic devices, beam-forming algorithms

I. INTRODUCTION

In recent years an increasing demand has interested airports and the related air traffic management authorities. The risk of collision between taking off and landing aircrafts and between aircraft and ground vehicles due to increased air traffic are issues that have highlighted the need to improve airborne surveillance systems by means of real time aircraft identification and tracking procedures in aerodrome zone. State-of-the-art ATM (Air Traffic Management) systems for aerodrome surveillance include especially radar technology. On the other hand, there is currently a significant increase of interest in the international scientific community, in the definition of alternative acoustic systems for locating, tracking and identification of moving acoustic sources with particular attention to aspects of intruder aircrafts and for monitoring the movement of ground forces. This interest stems primarily from the fact that unlike radar detection, acoustic detection can be performed with totally passive sensors only by listening to the noise of the target. This represent an obvious advantage from the environmental point of view (no emissions of any kind), safety (no possibility of locating the sensor in the absence of emission, inability to drastically reduce the noise of the target), and costs (reduced energy consumption due to lack of transmission power, lack of critical components working at microwave, etc.). In this paper, the preliminary results of MIUR (Italian Ministry of University and Research) - funded GUARDIAN project are reported. The aim of this research project is the design and development of a novel acoustic system for the improvement of co-operative management of ATZ (Aerodrome Traffic Zone) control. ATZ is the air space of defined dimensions interested by taking off and landing of aircrafts. The

proposed novel cognitive sensor offers flexibility and adaptivity to the airport scenario while ensuring a neat capability improvement in aircraft and ground vehicles detection, location and tracking.

II. DESCRIPTION OF THE ACOUSTIC SYSTEM

The GUARDIAN sensor is a multi-modal, in-air passive acoustic device working in arrayed/sparse configuration by means of an innovative ensemble of digital processing stages. This system allows the detection and tracking of in flight aircraft and in take-off or landing by means of a passive detection and spectral analysis of airplane acoustic emission.

Figure 1. Physical architecture of the GUARDIAN system

The system prototype consists of two planar acoustic sensors, one rotating (Master) and one fix antenna (Slave), based on a passive phased array of randomly distributed microphones and a command & control unit to allow the user to manage and visualize tracking data. Figure 1 shows the sketch of the system.

III. PHASED ARRAY DESIGN PARAMETERS

The performance of an acoustic array is determined by its geometry (shape, number and spatial position of microphones), which defines the response of the array, called Array Pattern. After fixing the geometry, the frequency and


the antenna pointing direction, the Array Pattern shows the attenuation performed by the antenna on the acoustic waves incident from directions other than the pointing direction. The MSL (Maximum Side lobe Level) value, which is the ability of the array in reducing false noise sources in directions other than the antenna pointing one, and the resolution, which defines the minimum angular distance at which the array is able to separate two nearby sources in space, can be deduced from the Array Pattern. Thus, acoustic array performance figures are: angular resolution (-3 dB main lobe width), MSL, maximum range and working frequency.

IV. GUARDIAN PROCESSING CHAIN

In present section, the Data Processor architecture is reported. Scope is to highlight the different functionalities that can be identified as sub-blocks of the processor and to describe their logical interconnections. Architecture describes the logic chain of the Processor and operates first important design choices that highly impact on resulting performances, complexity and computational load of the Processor. Architecture design is thus a critical phase and is best tackled keeping resulting schema as modular and scalable as possible. Figure 2 shows the GUARDIAN Processor architecture which is organized in the following sub-blocks: Data acquisition (i), Beam-forming (ii), Detector (iii), Triangulator (iv), Tracker (v) and Classifier (vi).

Figure 2. Logical architecture of the GUARDIAN processing chain

A. Data Acquisition Data acquisition is the first step in Data Processing. In

this phase, acoustic samples are collected in the time-domain at each array microphone and they are made available for further processing. The parameters relevant for this processing step are the time acquisition window, i.e. the time interval in which data are acquired, and the sampling frequency. In GUARDIAN system, the time acquisition window is equal to T = 0.2 seconds, while data sampling frequency is set to fs = 48 kHz. The data acquired by the acoustic antenna are processed with the FFT operation in order to translate them in the frequency-domain. The data collected in time-domain, which are in form of a [Array Elements x Time Samples] matrix, are then converted in a [Array Elements x Frequency Bins] matrix.

B. Beam-forming The operation of beam-forming has the scope of

electronically forming pointing beams for listening, searching and/or tracking purposes. The SOI (Signal Of Interest) for this kind of application is a broadband signal. Most of the energy contribution of aircrafts spectral signature is in the range 500 – 2500 Hz. For this reason, a frequency-domain multiple beam-forming approach is used in order to implement the acoustic data processing. Indeed a low computational cost is achieved by splitting the signal into its frequency components through an FFT and applying the beam-forming algorithm to each component. Given the planar localization of microphones on antenna array, spatial beams are synthesized along azimuth ( ) and elevation ( )direction by means of beam-forming operation. Acoustic data are processed in parallel through two different filters: the MBMF (Matched Beam-forming) and the MVDR (Minimum Variance Distortion less Response). This choice is due to the fact that MBMF and MVDR [1] exhibits different benefits. MBMF presents: (i) robustness to non idealities, such as steering mis-matches or the mis-positioning of array elements, (ii) low computational cost and an high-fidelity response i.e., the acoustic shapes are preserved for spectral analysis. On the other hand, it provides low accuracy, low resolution and it is sensible to the presence of interferences. Moreover acoustic classification issues may be faced by extracting spectral information from detected acoustic targets by implementing MBMF beam-forming in the angle/beam of detected target by means of MVDR algorithm. For all the mentioned reasons, to overcome the MBMF limitations, also the MVDR algorithm is implemented. It is an adaptive beam former which has high accuracy and high resolution. In addition, according to its adaptively, it is robust to scenarios which include interferences. However, it is computational expensive and it does not preserve the signal phases, i.e. the acoustic shape of signals is corrupted. Moreover, it requires the knowledge of the correlation matrix with interferences and noise; more in practice, since this knowledge is not given, correlation matrix must be estimated on the basis of the received signal. Due to their different properties, MBMF and MVDR are both performed with different aims:

- The MBMF is used for detection (its performance depends on circumstances) and classification purposes and it is performed on the entire band of interest (from 300 Hz to 3 kHz);

- The MVDR is used for detection purpose and it is performed only on a reduced band in which it is expected to find power for the aircraft (from 1 kHz to 1.5 kHz).

C. Delay and Sum Matched Beamformer In the matched beam-forming all microphone signals are

matched and summed in phase (coherent summation). For each looking (steering) direction time delays have to be applied corresponding to the arrival of a plane wave on each microphone. In the frequency domain a time delay corresponds to phase shift and therefore a frequency domain implementation is the simplest implementation. The DSBF


(Delay and Sum Beam-forming) in the frequency domain is formulated as:

=

⋅=N

nnnnf rk

cfjfXwfB

1),(2exp)(),,( ϕϑπϕϑ (1)

where:

• Bf is the beamformer output in the frequency domain;

• f is the frequency and k is the wave vector;

• , is the bearing to look at;

• n is the microphone number and N is the number of microphones;

• rn contains the coordinates of the n-th microphone;

• wn the MBMF weight (tapering coefficient) of microphone n;

• tapering coefficients are based on a Hamming (raised cosine) window;

• Xn(f) is the Fourier component of microphone n at frequency f. Xn(f) is derived from the microphone time-series xn(t) by FFT with NFFT points corresponding to integration time of tint = NFFT/Fs;

• c the sound speed value;

• beam-forming coefficients Cn can be computed in a fast way by iteration making use of the equidistant frequency grid. In this way no complex exponentials have to be computed: Cn+1 = Cn * Csince:

⋅Δ⋅⋅=⋅+ rkcfjrk

cf

jrkc

fj nn ),(2exp),(2exp),(2exp 1 ϕϑπϕϑπϕϑπ (2)

D. Minimum Variance Distortionless Response Beamformer The MBMF described in the previous paragraph is the

solution that maximizes the gain if the noise is uncorrelated (and no interferences are present). The adaptive beam-forming bases its weights on the data collected by the microphones (that’s why they are called ‘adaptive’) and consequently are scenario dependent. It can maximize the gain in case of presence of correlated unwanted disturbances impinging on the array. In GUARDIAN system, the adaptive beam-forming is implemented as an MVDR (minimum variance distortion less response) type of beam former in the frequency domain:

11 ),(2exp)(),(2exp),,(

−− ⋅−⋅= T

f rkcfjfrk

cfjfB ϕϑπϕϑπϕϑ R (3)

where:

• r is a vector of length N with the positions of the microphones;

• TfxfxfR )()()( = is the N*N covariance matrix of the microphones signals around frequency f , where the underline denotes an average.

In case of uncorrelated noise on the array, R = I and the adaptive beam former becomes a matched beam former.

E. Detection, Triangulation and Tracking The detector will run on the high resolution adaptive

beam-forming data and in parallel on the conventional beam former for a more robust detection. Indeed MVDR suffers low SNR targets but has low false alarm rate, while MBMF may have an high false alarm rate due to side lobes. Normally, when a target is detected in an adaptive beam, the nearest beam of the conventional beam-forming is used for acoustic classification. This approach reduces the false alarm rate. This detection algorithm consists of the following steps:

1. The beam energy 2

fB is integrated over a selectable frequency band; typically for the GUARDIAN system f =1000-1500 Hz. The selected band depends on the expected signal-to-noise ratios in the band, which will depend on aircrafts spectra, acoustic propagation, processing again, etc.

2. The resulting data in all beams are normalized by removing their median value or more in a more robust way by a moving mean.

3. In latter case, the outer beams should be removed from the further process due to edge effects of the filter.

4. Across all remaining beams, local maxima are identified. These local maxima are defined as those beams that have a higher energy than their two neighboring beams.

5. The local maxima are compared against a user-defined detection threshold (DT).

6. Local maxima that exceed DT are identified as detections.

The above 6 steps are carried out for both receivers at each temporal snapshot, resulting in two sets of DOAs (Direction Of Arrival) where aircrafts are detected. These two sets of bearings are then employed to find the actual position in Cartesian coordinates of the aircraft by means of a standard triangulation procedure. These plots feed the tracker which gives the aircraft track as output [2]. The track is the ultimate product of the Processor and can be complemented with the aircraft classification made by the Classifier. Finally, they are sent to the User interface and duly presented on the screen.

V. MICROPHONES DISTRIBUTION OPTIMIZATION

Due to the wide amount of optimisation parameters, (i.e. the microphones coordinates), an heuristic genetic approach was adopted (see flow chart in Figure 3), [3]. According to the natural selection principle, an initial population constituted by a certain amount of individuals, each one characterised by a specific genetic (chromosomes) is


subjected to environmental aggressions of different type (predators, hostile weather, diseases…). The higher the individual adaptability level, the higher the surviving chance and the possibility of transmitting own genetic to the next generation. This transmission occurs through the cross-over process: each individual, on the bases of its transmission chance, will interchange a part of the chromosomes with another individual, thus generating a new one, potentially better. By repeating this process (selection and cross-over) for an adequate number of times, the average performance (adaptability or fitness) of the population is increased. Anyway, since the first population contains a finite number of information and the next ones result just from a recombination of it, the improvement possibility is in practice confined to the specific initial chromosomes values. Hence, to hinder the premature convergence to a local maximum and to enrich the genetic content of a population, another phenomenon acts together selection and cross-over: the mutation, i.e. the random and spontaneous variation of a small part of the genetic code of some individual. Thus, individuals slightly different enrich the current population, guaranteeing a wider genetic content. Aforementioned logic was implemented for the antenna optimisation problem. Each population is constituted by a certain amount of antennas (individuals), differing each other for microphones abscissas and ordinates (chromosomes). The Maximum Side Lobe Level (MSL) in dB was assumed as fitness function: the lower the MSL, the better the performance. Constraints were taken into account both for the optimisation parameter domain (microphone locations are onto the plane of the antenna) and for the global performance of the antenna. In practice the locations of the microphones belong to an uniform square grid, while a cut-off level for the angular resolution was set, thus discarding antennas with unacceptable resolution, even exhibiting a good MSL. In order to fully investigate the dependence of the array performance on antenna shape and dimensions, two different sizes of the array have been investigated, both made of 256 microphones. The smaller was a 4 x 4 square meters array, while the larger was a 6 x 6 square meters one. First of all, a regular grid array fulfilling the array performance was designed. In particular, the fixed distance between adjacent microphones has been derived by the following formula (assuming fmax=1250 Hz), [4] [5] [6]:

max

min

22 fcdd ≤≤ λ (4)

while the side length D, linked to the desired resolution at the minimum frequency of interest, has been derived from:

( ) min

3cos1

fc

RzDθθ

= (5)

At low frequency, if a good resolution is required, the combination of a large antenna diameter and a small microphones distance causes the need for a large number of sensors. In order to reduce the number of microphones, only n=256 microphones positions are randomly chosen from all possible N locations (N=3600 for the larger array, N=1600 for the smaller array, thus n << N) of the regular mesh. An

optimization process was then performed to determine a sparse array configuration with the best possible combination of antenna performance parameters.

In TABLE I. adopted simulation parameters in the optimisation procedure are summarised.

TABLE I.

Algorithm settings

Parameter Smaller array (4 x 4 m) Bigger array (6 x 6 m)

Number of iterations (populations) 217 46

Number of individuals 4000 4000

Number of chromosomes per individual (x,y, coordinates of 256 microphones)

512 512

Cross-over % (chromosomes interchange percentage)

50 50

Mutation % (percentage of mutated individual per population)

10 10

Optimisation constraints

Microphone on a uniform square grid (m)

grid step: 0.1

grid dimension: 4 x 4

grid step: 0.1

grid dimension: 6 x 6

Resolution (deg): max (elevation, azimuth) 6 4

Figure 3. Genetic algorithm flow chart.


Several optimisation processes were carried out, moving from different initial populations. Figure 4 gives an idea of the convergence ability of the algorithm for some of these processes. At the end, different optimal results were achieved, each one characterised by the minimum MSL compatible with assumed constraints.

The optimal individuals were located onto a 2D graph (see Figure 5 for the bigger antennas), whose axis refer to MSL and angular resolution. In the same pictures, also results achieved from a wide amount of random no optimised configurations are reported, to highlight the quality of the results obtained through the optimisation process.

0 20 40 60 80 100 120 140 160 180 200 2200

2

4

6

8

10

12

14

16

18Fitness function vs iteration for the 6x6 m Grid Random Array

Population number

Fitn

ess f

unct

ion

(dB)

MaxMean

Figure 4. Fitness function vs iteration for the 6 m square antenna.

Figure 5. 100.000 randomly generated arrays (GREEN cross) Vs. optimized configuration (RED star) – Size 6x6 m.

VI. ARRAY PERFORMANCES

Figure 6 and Figure 7 summarise optimized antennas performance parameters (angular resolution and MSL) vs. elevation angle of antenna pointing direction in the case of MBMF approach. According to the theory, the angular resolution strongly decreases with the elevation steering angle, while MSL, mainly depending on sensors number, does not exhibit large variations.

Figure 6. Larger antenna performance parameters

Figure 7. Smaller antenna performance parameters

The corresponding optimal microphone distribution is depicted in Figure 8 and Figure 9.

Figure 8. Larger antenna optimal microphones distribution


Figure 9. Smaller antenna optimal microphones distribution

TABLE II. and TABLE III. summarise antennas performance parameters for a MBMF approach. The elevation linear resolution has been evaluated at a distance of 7 km. The range of the antenna, i.e. the maximum distance in the steering direction at which the array can yet detect the sound source, has been determined by considering a tonal (with different frequencies) sound source with an SPL of 140 dB at 10 m and a tonal (at the same frequency) background noise with an SPL of 60 dB. Only the sound attenuations due to air absorption (frequency dependent) and spherical divergences were considered. As reported in TABLE IV. and in, a range of 7 km can be theoretically achieved for a sound source with a tonal frequency of 1250 Hz and an array with MSL of -16 dB (i.e. with 256 microphones).

TABLE II.

Steering elevation Elevation angular Elevation linear MSLangle (deg) resolution (deg) resolution (m) @ 7km (dB)

30 2.77 338.68 -16.0760 4.80 587.81 -16.36

GRID RANDOM ARRAY (6x6 m)

TABLE III.

Steering elevation Elevation angular Elevation linear MSLangle (deg) resolution (deg) resolution (m) @ 7km (dB)

30 3.96 484.58 -16.7960 7.07 868.17 -16.72

GRID RANDOM ARRAY (4x4 m)

TABLE IV. Sound source-array Spherical divergence Air absorbing Gain over Background

distance (m) attenuation (dB) attenuation (dB) Noise (dB)10 0.00 0.05 95.9520 6.02 0.10 89.8840 12.04 0.20 83.7680 18.06 0.40 77.53100 20.00 0.51 75.50200 26.02 1.01 68.97400 32.04 2.02 61.94500 33.98 2.53 59.50

1000 40.00 5.05 50.952000 46.02 10.10 39.883000 49.54 15.15 31.314000 52.04 20.20 23.765000 53.98 25.25 16.776000 55.56 30.30 10.147000 56.90 35.35 3.758000 58.06 40.40 -2.469000 59.08 45.45 -8.5310000 60.00 50.50 -14.50

Gain over Background Noise @ array - Tonal noise=140 dB @10 mArray gain=16 dB - Background noise=60 dB - Air: 15 °C & 70%U.R.

-10.00

0.00

10.00

20.00

30.00

40.00

50.00

60.00

70.00

80.00

90.00

100.00

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 10000Sound source-array distance (m)

Gai

n V

s. Ba

ckgr

ound

noi

se (

dB)

500 Hz1000 Hz1250 Hz2000 Hz4000 Hz5000 Hz

Figure 10. Array gain over background noise Vs. frequencies

VII. CONCLUSIONS

The GUARDIAN prototype system based on planar acoustic antenna has been described, showing the results of beam-forming technique for aircrafts detection.

The results herein illustrated proved the possibility of using microphone arrays for air traffic control inside ATZ. However, in order to achieve performance parameters comparable to those of conventional radar, the use of antennas with great diameter (tens of meters) and with a large number of microphones (1000) is required. The use of MEMS (Micro Electro-Mechanical Systems) digital microphones will make antennas of this typology economically and technically feasible.

For what concern the improvement of the array resistance to harsh environmental operating conditions usually found at airports (high humidity, rain, wind, low and high temperature,…), the development of innovative antenna employing FBG (Fiber Bragg Grating) sensors will allow the implementation of permanent outdoor stations.

ACKNOWLEDGMENT

The product was developed within the MIUR-funded GUARDIAN project.

REFERENCES

[1] H.L. Van Trees; Detection, Estimation, and Modulation Theory, John Wiley & Sons, 1968

[2] Y. Bar-Shalom et al., Estimation with Applications to Tracking and Navigation, Johon Wiley & Sons, 2001

[3] “Genetic Algorithms in Search, Optimisation & Machine Learning”, David E. Goldberg, 1989, Addison-Wesley Publishing Company, Inc., pp. 60 – 88.

[4] J.J. Christensen and J. Hald, “Beam-forming”, Technical Review N. 1 2004, Bruel & Kjaer Sound & Vibration Measurement A/S, DK-2850 Naerum, Denmark.

[5] C.A.Balanis “Antenna Theory Analysis and Design”, Third Edition, a John Wiley & Sons, Inc., Publication.

[6] M. Brandstein and D. Ward, “Microphone Arrays”, Springer, Berlin 2001, pp. 157-177.


On board PCL systems for airborne platform protectionSession 5.1 page 119

FM Based Passive Coherent Radar.From detections to tracks


High Range Resolution Multichannel DVB T Passive Radar:Aerial Target Detections


Range Only Multistatic Tracking in ClutterSession 5.4 page 133


On-board PCL systems for airborne platform protection

Krzysztof Kulpa, Mateusz Malanowski, Piotr

Institute of Electronic Systems Warsaw University of Technology

Warszawa, [email protected]

Maciej Smolarczyk Pr

Warszawa, POLAND

Abstract The paper presents the idea of application of passive radar (PCL) on board of an airplane. The aim of the PCL system is to provide surveillance information to the pilot (or autopilot), to detect the targets in the surveillance space and to protect the platform from collisions and other threats. The PCL system can use the available emitters of opportunity, mainly DBV-Ttransmitters. Other sources of illuminations, such as analogue(FM) or digital radio (DAB) can be used as well.

Keywords: passive radar, PCL, air survailance

I. INTRODUCTION

The safety of aviation is one of the most important issues in the last century. Different technologies were used to improve the air safety and to avoid collisions, especially at night and during bad atmospheric conditions. One of the most important technologies used for that purpose is radar technology. At the beginning, the powerful primary surveillance ground based radars was applied. The on-board radar systems were expensive, very complicated and heavy, so only combat aircraft were equipped with active radars.

The application of active transponders, replying to requests of secondary radars and transmitting their own position obtained from on-board GPS receiver improved significantly the safety and quality of information about all users of the air zone. However, this technique does not eliminate many threatssince non-cooperative targets do not respond and it is difficult to avoid collisions with such objects.

In the last decade, the rapid progress in Unmanned Air Vehicles (UAVs) can be observed. The manned remote control of such objects is possible only for small distances, when optical visibility between UAV and operator is ensured. The use of on-board optical cameras can help significantly, but this technology is limited by down-link properties and fails duringbad weather conditions and low light during night-time.

The situation can be improved by adding active radars which provide situation awareness to auto-pilots to help in avoiding collision. The required instrumentation range of the radar depends on the maximum relative speed between the targets, radar scan time and assumed reaction time of the platform. Assuming relative speed between targets at the order of 600 m/s, radar scan time 5 s and required reaction time 5 s,

the minimal radar detection range should be not smaller than 12 km (assuming 3 scans for proper target behavior identification). The detected RCS should be at the level of -20..-10 dBsm to detect not only aircraft brequirement leads to radar devices with mean transmitting power of 20..100 W with advanced antenna and signal processing system. The extensive application of active radar systems will lead to increase of electromagnetic ,and while many similar radars can work in the same band, to serious interference problem.

An alternative approach uses the passive radar concept. Because the passive radar does not emit any energy, it can be small, low-weight and with low power consumption, required only for data acquisition and processing block. However, to make reliable on-board passive radar, several theoretical and engineering problems must be solved.

II. THE CONCEPT OF ON-BOARD PCL SYSTEM

The on-board passive radar has to receive signals from all available emitters of opportunity and observe the area ofinterest to detect the potential threats. It requires multi-beam adaptive antenna arrays to track illuminators and targets in the vicinity. While passive radars are highly dependent on emitters of opportunity, the flight-path should be planned in such way, that proper radar coverage is available during whole mission.

A. Selections of illuminators of opportunityThe passive radar does not illuminate the targets but

exploits the commercial transmitters of opportunity, existing in the area of interest. The transmitters should have sufficient emission power, good transmitted signal cross-ambiguity function and high enough frequency. The requirements of high transmitting power is related to the predicted detection range described by the following relation

i

o

RT

AOTT

tkTND

rrSSGP

222)4(, (1)

The work is partially sponsored by Polish Ministry of Science and Higher education under grant no 5525/B/T00/2010/39. This work was partially supported by the European Union in the framework of European Social Fund through the Warsaw University of Technology Development Programme, realized by Center for Advanced Studies.


where TP is the transmitted power, TG is the transmitter

antenna gain, OS is target radar cross-section, AS is effective

surface of radar antenna, Tr is transmitter-to-target distance,

Rr is the target-to-radar distance, k is Boltzmann constant, T is the equivalent receiver temperature, N stands for total loses, oD is detection threshold (usually 13 dB) and it is integration time.

The requirement of proper shape of cross-ambiguity function is related to range and Doppler resolution of the radar. The good cross-ambiguity function means, that there existssharp peek of cross-ambiguity function and the side-lobes which are small enough to make detection of small targets possible in case of heavy clutter. The range and velocity resolution should be comparable with modern active radars; required accuracy in range is 10..200 m and 1..5 m/s in velocity. Additionally, the properties of cross-ambiguity function should be time-invariant, to ensure the required performance over 24h/day.

The existing PCL radars [2] are the ground base ones exploiting FM radio emission [1, 5, 9, 10]. There exist also several PCL radar demonstrators exploiting other emissions such as digital terrestrial TV (DBV-T) [4, 8], digital radio (DAB) [3, 6, 8], satellite TV (DVB-S), analogue [7] , cellularphone networks (GSM) [18], [19], internet links (WiFi, WiMax) [20] and many more. The possible PCL detection ranges using selective, 30 dB gain receiving antennas are plotted in Fig. 1.

Figure 1. Selection of emmiters of oportunity

The aforementioned requirements for airborne PCL systems limit in practice the choice of transmitters of opportunity to the ones with powerful digital continuous emission. Presently there exist 2 candidates: digital terrestrialTV DVB-T and digital radio DAB. The illumination from satellites: digital satellite television DVB-S and global positioning GPS have small transmitting power, thus the

receiving antenna system will be complicated and heavy, possible to install only on big man-operated aircraft. Due to low power density the practical detection range will be limited to c.a. 4 km but coverage is very good while practically whole land areas of the world are covered by satellite TV and whole globe is covered by GPS. Moreover, the coverage of GLONAS and GALILE is expanding.

The DAB emission is one of the potential candidates with 1.5 MHz bandwidth and related 100 m range resolution. For the DAB emission two bands are designated UHF: 174 -240MHz (replacing TV channels 5..12) and L: 1452 1492MHz.The first one is better suited for bigger platforms, while the

antenna element is 40 cm long, while the second, not popular at the present time, can be applied for UAV in the future.

The DBV-T is now under rapid development and in wholeEuropean Union countries it will replaced analogue TV within couple of next years. The typical frequencies are 400-800 MHz, transmitted power 10-100 kW and bandwidth about 8 MHz. The range resolution is 20..30 m, almost independent ofthe contents of transmission. The velocity resolution depends on integration time and resolution of 1 m/s can be reached. The single antenna elements are of size of 10-20 cm, therefore it is possible to construct the antenna array also on medium size UAVs. To increase the possible number of channels available for all TV viewers, the single frequency network concept is applied in DVB-T. All transmitters within the country transmitting this same programs are using the same frequency.All transmitters are synchronized in time and frequency by GPS signals. Thus single receiving channel in passive radar can be used, while the target is illuminating by several transmitters.

B. PCL processing conceptThe passive radar detect the targets by calculating the

cross-ambiguity function

it tc

Fvj

TR dtecrtXtXtwvry

0

2* )()()(),( (2)

where *TX is the reference signal, received by the beam

directed towards transmitter, )(tX R is the signal from

surveillance beam, it is the integration time, F is the carrier frequency and )(tw is weighting function. The target bistatic range r and velocity v are estimated as the coordinates of the maximum of function given by (2). The Cartesian coordinates of detect targets are calculated by finding the cross-section points of at least 3 ellipsoids of bistatic distances r and focal points placed in the transmitter and radar positions. Thus the own platform position (e.g. from GPS) and position of all transmitters of opportunity must be known.

The concept of airborne PCL system is presented in Fig. 2.The illuminators of opportunity are the DVB-T transmitters, while they have sufficient transmitting power, good, time-invariant cross-ambiguity properties and good coverage.


The required detection range is almost one order of magnitude smaller than theoretical detection range, predicted by formula (1) and presented in Fig. 1, so the problem of low elevation coverage of DVB-T is not of high importance, while it is possible to exploit vertical side-lobes of transmitter antenna for illumination. The single-frequency network is a benefit, while it is possible to tune the radar receivers only to one frequency and obtain multi-transmitter tracking system.The typical integration time is 0.1..1 s so tracking can reach required accuracy within 1-5 s, and thus reaction time is spedup by 3 times. In such case the detection range can be shortened to c.a. 6 km. The targets location is estimated calculating ellipsoids crossing, as stated in Fig. 2.

One of the basic problems in passive airborne radar is cancelation of ground clutter. In ground-based PCL system ground clutter has no Doppler spread and can be easily canceled using adaptive lattice filter [14]. In PCL radar placed on moving platform clutter signal has significant Doppler spread caused by the platform motion. The clutter-free region is limited in range by the platform elevation (when only direct signal compete with the target echoes) and in Doppler by platform velocity. Using classical Doppler spread clutter cancelation method described in [17], not only clutter but also the targets of interest can be canceled.

Figure 2. The airborne PCL scenario.

The other possibility is to apply adaptive space-time processing (STAP) to canceled the ground clutter. The classical STAP algorithms are derived for active radar, where all sounding pulses are the same. In PCL system the illuminating signal is continuous and due to changes of information contents, the time shifted parts are not identical. As the results,STAP algorithms for airborne PCL systems have to be modified to obtain required properties.

III. EXPERIMENTA RESULTS

To gain deeper knowledge of the PCL signal properties the set of experiments were performed at Radar Technology Lab at Warsaw University of Technology (WUT). The first part of the experiments were performed using Raw Radar Signal Simulator, simulating the scenario using FM radio and DVB-Tsignals for scene illumination and 6 elements antenna array.

The signals from all antennas were amplified by selective FM band COTS amplifiers (88-108 MHz) and directly digitized.The digital signals were down converted to the baseband where IQ digital decoding was perform. The IQ signals from 2 selected FM channels (100 kHz each) were recorded on the hard drive, and processed offline.

The experiments proved that the direct use of classical STAP do not ensure good results, while combining the CLEAN technique [17] for direct signal cancelation with modified STAP approach gives significant improvement. Up to now only FM based experiments on moving platforms, namely on car platform [11] and on BRYZA airplane [12] were carried out at WUT Radar Technology Lab (Fig. 3).

Figure 3. Bryza airplane (left) and the FM antenna mounted temoprarly at the plane window (right)

The result of signal processing using combination of both methods is presented in Fig 4. It can be seen that the residual clutter returns are present in the picture, but also the target is clearly seen.

Figure 4. Detection results using CLEAN method combined with simplified STAP amplituse (in dB scale) color coded


The residual clutter can mask some of the slow targets, so further development of efficient signal processing algorithms isrequired.

The experiments with DBV-T illuminators are scheduled for summer 2011.

IV. CONCLUSIONS

The paper presents the idea of Airborne Passive radardedicated for aircraft and UAVs platform for air surveillance for the purpose to increase air safety and avoid collisions in air-space. It can be used for men-operating platforms, but main benefits will be to connect the PCL radar output to autopilot to provide safety for unmanned platforms. The first trials on passive airborne radars performed in Poland [12, 16] and England [13] shows that this technology can be used in near future.

ACKNOWLEDGMENT

The authors would like to thanks Polish ministry of Science and High Education for support under scientific grant 5525/B/T00/2010/39 and Polish Cost Guard for cooperation.This work was partially supported by the European Union in the framework of European Social Fund through the Warsaw University of Technology Development Programme, realized by Center for Advanced Studies.

REFERENCES

[1]

pp. 107 115, June 2005.[2] B. D. N

Weekand Space Technology, no. 30, pp. 70 71, 1998.[3] -path filtering of

vol. 39, Issue: 1, 9 Jan. 2003, pp:118 119.[4]

2006, Cracow, Poland, 24 26 May 2006, pp. 657 660.[5] A. D. Lallo, A. Farina, R. Fulcoli, P. Genovesi, R. Lalli, andR.

based 30 May

2008, p. CD.

[6]CoherentLocation (PRadarCon,Rome, Italy, 26 30 May 2008, p. CD.

[7] -IEE Proc.-Radar, Sonar & Navig., Vol. 146, No. 3, June 1999.

[8] H. Kuschel, M. Glende, J. Heckenbach, S. Mller, J. Schell, and C.

2007, Cologne, Germany, 5 7 September 2007, pp. 411 417.[9] M. Malanowski, K. Kulpa, and J. Misi Passive

Radar Demonstrator family development at Warsaw University of

Ukraine, 22 24 September 2008, p. CDC. [10] t

Sonar and Navigation, vol. 152, no. 3, pp. 160 168, June 2005.[11]

um 2008, Wroclaw, Poland, 21 23 May 2008, pp. 305 308.

[12] K. Kulpa, M. Malanowski, J. Misiurewicz, M. Mordzonek, P. Samczyski,primary Military Radar 2008, Amsterdam, The Netherlands, 27 29 October 2008, p. CD.

[13]

LETTERS, vol. 46, no. 20, September 2010.[14]

International Conference on Radar, Tuluse, France, 18 22 October 2004, p. CD.

[15]based2007, Cologne, Germany, 5 7 September 2007, pp. 431 435.

[16] M. Malanowski, K. Kulpa, M. Mordzonek, and P. Samczynski,

Specialist Meeting SET-136, Lisbon, Portugal, 23 25 June 2009, p. CD.[17]

International Radar Symposium 2006, Cracow, Poland, 24 26 May 2006, pp. 299 302.

[18] features 2nd Passive Radar FHR-PCL-focus day(s), 17-18

November 2009, Wachtberg, Germany, pp. CD[19] P. Samczynski, K. Kulpa, M. Malanowski, P. Krysik, ,

A Concept of GSM-based Passive Radar for Vehicle Traffic Monitoring , to be published in proceedings of MRRS-2011, 25-27 August 2011, Kiev, Ukraine.

[20] F

FHR-PCL-focus day(s), 17-18 November 2009, Wachtberg, Germany, pp. CD


FM Based Passive Coherent RadarFrom detections to tracks

Advanced programsERA a.s.

Prumyslova 387, Pardubice, Czech [email protected]

Abstract This paper provides description of individual steps leading to successful targets tracking in multichannel passive coherent system. Emphasis is given on targets association principle in bistatic space prior to Cartesian tracking. The angle of arrival from antenna array as a mean of targets association and its properties is discussed.

Keywords - Passive Coherent Location; multistatic radar;tracking; Angel of arrival

I. INTRODUCTION

A long term experience with military Electronic Support Measurement (ESM) systems like Tamara and recent Vera-Nwas motivation for company to follow with research dedicated to passive radars technology usually called Passive Coherent Location (PCL). In contrary to ESM system which exploits different kind of signal emissions generated by target, the PCL utilizes reflected signal and therefore it is able to detect non cooperative and quiet targets by means of bistatic Radar Cross Section (RCS). Bistatic configuration is also very suitable for detection of stealth targets because their primary focus is on monostatic RCS minimalization and property of non electromagnetic emissions which makes them undetectable. Moreover, the PCL system can utilize wide range ofcommercial used illuminators of opportunity within VHF and UHF bands.

Figure 1 Passive Multistatic Coherent Radar (one receiver with multiple transmitters configuration).

II. SYSTEM CONCEPT

The PCL system being under development in ERA consists of one receiver site and it is proposed to process signal from four transmitters of opportunity, hence the system falls in the group of multistatic configurations (see figure 1). It works with signals intended for commercial terrestrial radio broadcasting in band from 88 MHz to 108 MHz, where speech or music is frequency modulated with ca. 100 kHz bandwidth. Conception

frequency down-conversion, is being used. Therefore, only a very simple analogue front-end is needed which consequently

A. Analog signal processing

Figure 2 Individual elements pattern (left) and combined patterns after applied beam forming (right).

The proposed system consists of circular antenna array with eight horizontally placed elements. Each element is connected into signal divider which gives arise of required number of output signals. It is possible to slightly reduce required high dynamic range with analogue beam forming technique.Thereforefrom adjacent dividers) is connected to passive beam-former consisting of delay links and attenuators network. With proper beam-former settings, the minimum of radiation pattern derived from combined antenna elements (pair) is set to direction of the transmitter whose signal is being processed (see figure 2). After that, all signals are passed through filter

ends with an Analogue-to-Digital Converter (ADC). All subsequent operations on signals (radio channel selection, filtration, etc.) are made digitally. Since the output signal


bandwidth is ca. 150 kHz, signals are decimated, and therefore, the native sampling ADC speed is reduced from 125 MSps to approx. 240 kSps and complex signal is generated.

B. Digital signal processingDigital signals in complex form are moved forward to

sophisticated digital signal processing chain which is appropriately divided into two basic blocks: Multichannel coherent processing and Air picture acquisition (see figure 3for more details).

Figure 3 Signal processing chain block diagram

Signal from each antenna pair is processed separately on Graphic Processing/Central Processing Units starting with ground clutter rejection based on adaptive filtering within using the auto-ambiguity function of reference signal. At this stage, the static targets (clutter) are removed from the signal and only moving targets remain. Subsequently the matched filter is applied i.e. Cross Ambiguity Function (CAF) is computed for each processed signal whereas peak in CAF represent elliptical distance and elliptical Doppler frequency shift of appropriatetarget. The longer is the integration interval, the lower (hence better) the sensitivity of the system is, however its maximal length is limited by required refreshing rate of the system (bellow one second typically)maneuverability possibilities. It is reasonable to notice that different kind of modulation signals (speech vs. music) lead to different power of ambiguity function side lobes and for that reason Sequential Target Elimination algorithm is applied instead of widely used simple Constant False Alarm Rate detector (CFAR) to discover possibly hidden peaks in CAF surface.

Every single detected peak is equipped with a number of parameters and such detections, or plots, are further processed by Air picture acquisition block.

C. Coverage prediction

coverage, can be roughly predicted considering transmitter/receiver geographic location and other parameters. Taking into account omnidirectinal radiation pattern for both transmitting and receiving antenna together with their real location, it is possible to obtain signal to interference (reflected to reference signal) . It

can be seen impossibility to detect any target with such system. On the other hand, the situation will change significantly by addition the adaptive clutterrejection (ca. 40 dB depends on modulation type), CAF (ca. 48 dB depends on number of samples entering each

10 dB depends on particular pair of antenna elements). Contours of the minimum required RCS to let the target be detectable by system with consideration of all mentioned effects are depicted in figure 4 for two transmitters. The

behind transmitter is easily observable. Note both subplots in the figure are results of real radiation pattern measurements and their adjustment by beam-former.

Figure 4 Covergade predictions: minimal detectable RCS [dBsm] contours for current antenna patterns and beam-former settings for Krasne (left) and

Cerna Hora (right) transmitters

III. DETECTIONS

The system is still under development, and therefore its efficiency is measured by means of only one FM radio processing at the moment. Detections across all individualprocessed channels (i.e. signals from individual elements)during ten minute record are depicted in figure 5 as an example

There is significantly lower number of detected targets in channel number 8. This is given by fact, that this signal has the same contents as reference one. The figure 6than, contains detections from all channels joined in single Range vs. Doppler plane. Note several detections in bistatic range up to 260km observed.


Figure 5 Detections in individual channels (top to bottom channel 1 to channel 8)

Figure 6 Detections from all channels in single range vs. Doppler plane

Since exact target 3D positions cannot be determined by

exact target positions in Cartesian space was involved into testing.

Surveillance System (MSS) which is situated in area of Pardubice in the Czech Republic. Its coverage is angularly dependent, typically 400 km in range. Comparison between PCL and MSS is performed in range-Doppler plane, when a real time data from MSS system are converted from Cartesian into this coordinate system. Each detected PCL target is then associated with its nearest MSS target if they meets condition of minimal range-Doppler distance. This threshold is formed by an ellipse which center is situated at location of any MSS target, and its semimajor axis is 3 km and semiminor axis is 10 Hz. The PCL system efficiency is than evaluated in such manner. Target association also provides overview of detected targets in Cartesian space (see example in figure 7).

Figure 7 PCL detections associated with MSS ground truth (transmitter

IV. ANGLE OF ARRIVAL

The Angle of Arrival (AoA) as a mean of targets association from different FM channels could be derived based

and amplitude conditions among individual antenna array elements and their radiation pattern knowledge.

Let us mark vectors representing complex samples of signals from each channel (i.e. antenna element or beam former output) as ( where n is a number of channels(8 for discussed system). Estimation of phase differences and amplitude ratios is based on minimizing the sum of energy of signals defined as

where are complex coefficients representing the amplituderatios and phase differences among channels, i.e.

, and represents the best estimate of the input signal (signal with highest SNR is taken at first iteration).

Hence, we are minimizing two variables function according to relation

It has been already mentioned in previous chapter, that all possible antenna phase and amplitude are manipulated to achieve a combined radiation pattern with the null in the transmitter angle. This conveys the advantage in increasing the signal-to-interference ratio. On the other hand, it is expected that the relationship among individual channels will not be straightforward as well as, and due to the fact, that the phase center of each elements pair will not remain in fixed location. To overcome this issue,the pre-computed table of vectors k is proposed. item than represents one direction which the signal is received


from. Before the system is fully operational, the table is filled with pre-computed values based on estimated antenna radiation pattern (via 3D electromagnetic field simulator fed with detailed antenna model) together with known beam-formers setting. Afterwards, the table shall be corrected by values from real measurement.

Number of table rows determines the number of segments that the space is divided in. Almost equally distributed slicescan be established similarly to (ommatidium), by consecutive division of sphere. This is madewithin use of Platonic solid, particularly decahedron, which allows us to divide its basic 12 surfaces into arbitrary number of sub-cells and hence obtain required angle resolution (see figure 8).

Figure 8 Left - decahedron surface with two different angle resolutions.Right angle resolution for different number of used ommatidia (legend

describes angular resolution in degrees with overall number of segments per sphere behind slash)

At first, emphasis has been given to investigate the stability behavior of phase and amplitude ratios among channels. This provides us with an overview of expected AoA determination efficiency. Evaluation has been made on track-by-track basis within help of reference ground truth tracks as described in chapter III. Only tracks with simultaneous detections in more than three channels are utilizable, hence considered, and differences among them analyzed for whole interval that the target is detected by PCL and/or tracked by MSS. There is an example of differences evaluation for particular target detected in most of the collecting intervals from period about 240seconds in the figure 10.

signal-to-noise ratio considering all processing gain is shown in figure 9. It can be seen, that the target is about 50 km apart from receiver in average and estimated SNR varies from 6 dB up to 22 dB.

Figure 9

The figure 10 shows complex plane histograms of ratios between estimated coefficients ki in two consecutive collecting intervals, related to the channel number 6, which in this case is the channel with strongest signal. Hence, the histogram for channel 6 contains only one peak representing difference zero (i.e. zero phase difference and equal amplitude) as a reference.Only 7 out of 8 channels are shown, because channel nr. 8 havethe same contents as the reference one and so any targets have been detected here. Despite of all histogram should optimally look like channel nr.6, i.e. the variances from one collecting interval to another would be negligible as the AoA remains almost the same no matter what actual channel is evaluated,some differences can be observed.

Figure 10 Differences of complex determined komplex coefficients related to channel nr.6 (channel nr.1 on the top to nr.7 at the bottom)

V. CONCLUCIONS

In this paper the FM based PCL system, still being under development in ERA a.s., is described. Its prototype covers circular antenna array with 8 dipole elements, analogue processing chain realizing beam-forming, direct sampling digital receiver as well as powerful processing running on GPUs. Example of detections from single FM channel is


shown and efficiency is evaluated within help of reference ground truth multilateration system.

As the angle of arrival is supposed to be used for detection association among different FM channels, its performances are investigated. It has been shown in the last chapter, that the AoA contribution should be utilizable in tracks forming process.

REFERENCES

[1] , L., K , M., Pelant, M., V , J., P , R. Simulation and Evaluation of the Passive Coherent Location system. In Proceedings of International Radar Symposium. Germany: Berlin, 2005.

[2] - Stejskal, V. - Pelant, M. - -Based Passive Coherent Location System, Detection and Accuracy. In Proceedings of the International Conference on Military Technologies 2009. Brno: University of Defence, 2009. pp. 448-457. ISBN 978-80-7231-649-6.

[3] Stejskal, V. - -Based Passive Coherent Location Demonstrator. In Proceeding International Symposium - Enhanced Solutions for Aircraft and Vehicle Surveillance Applications. Berlin, March 2010.


High Range Resolution Multichannel DVB-T Passive Radar: Aerial Target Detection

D. Petri, A. Capria RaSS National Laboratory

CNIT (National Inter-University Consortium for Telecommunications)

Pisa, Italy {dario.petri, amerigo.capria}@cnit.it

M. Conti, F. Berizzi, M. Martorella, E. Dalle Mese Department of Information Engineering

University of Pisa Pisa, Italy

{m.conti, f.berizzi, m.martorella, e.dallemese}@iet.unipi.it

Abstract— A method for improving the range resolution in passive radar systems is to jointly use more than one transmission channel of the same Illuminator of Opportunity (IO). This paper specifically focuses on the exploitation of three adjacent Digital Video Broadcasting - Terrestrial (DVB-T) channels for achieving high range resolution profiles with a passive radar system operating in air surveillance scenarios. Firstly, an analysis of the ambiguity function obtained from a multichannel DVB-T source is presented. Afterwards, the experimental scenario is defined and a preliminary detection result on aerial targets is shown.

Keywords- passive radar; multichannel; DVB-T; aerial targets, air surveillance, HRRP;

I. INTRODUCTION

Passive radar systems (also referred to as Passive Coherent Location) exploit Illuminators of Opportunity (IO) such as radio FM, analogue and digital television transmitters, GSM/UMTS base stations etc, in order to detect and track targets. This system concept has a great interest for both civilian and military scenarios mainly due to a number of advantages with respect to active radar as low cost, low weight and enhanced radar cross section for certain geometries. Moreover, it does not require any dedicated frequency band allocation and it guarantees low Probability of Intercept (LPI) thanks to the absence of the transmitter unit. The performances of a passive radar system mainly depend on the transmitted power and on the characteristics of the exploited IO as reference signal. As the range coverage strongly depends on the transmitted power level, highly powerful transmitters, such as broadcast FM, DAB radio and analogue or DVB television transmitters are to be preferred. Regarding the waveform suitability for radar purposes, the ambiguity function provides a mathematical tool for radar designers to identify resolution and ambiguities in both delay-time and Doppler [1][2][3]. The exploited waveforms can be divided in two main classes: analogue or digital waveform. It is worth noting that the ambiguity function of analogue sources (e.g.: FM radio or analogue TV) is unpredictable as it is the result of a signal time-varying structure, which typically produces a content dependent signal bandwidth. On the contrary, digital waveforms exhibit an ambiguity function with a thumb-tack shape and a bandwidth that is constant in time. Furthermore, in many

countries, the analog radio and TV transmissions are scheduled to be dismissed and to be replaced by digital ones. Consequently, DVB-T transmitters are certainly good candidates for passive radar purposes thanks to the high level of radiated power and the good waveform performances in terms of range and Doppler resolution. An ongoing research field about passive radar systems concerns the theoretical range resolution improvement by using multiple FM channels [4][5] and DVB-T channels[4]. For example [4] gives a mathematical framework to deal with equally and not-equally spaced FM radio or DVB-T channels. In a previous work [6], two approaches to achieve high resolution exploiting multiple adjacent DVB-T channels of the same transmitter have been presented. This paper analyzes the application of these techniques to real data. Moreover, preliminary detection results in an air surveillance scenario will be shown. This paper is organized as follows: in section II a comparison between single channel DVB-T ambiguity function and multichannel DVB-T ambiguity function will be presented and analyzed. Then, the acquisition system and the experimental scenario are described. Finally real data results are presented and discussed in order to evaluate the system performance in terms of spatial resolution.

II. MULTICHANNEL DVB-T SIGNAL: AMBIGUITY FUNCTION ANALYSIS

A multichannel DVB-T signal can be analytically modelled as:

( ) ( )1

2

0

cm

Nj f t

ref mm

s t e s t e π−

=

= ℜ (1)

where Nc is the number of channels, fm is the carrier frequency for the m-th channel and ( )ms t is the complex envelope of the m-th channel. Under the assumption that the Nc channels are equally spaced, it is possible to write fm as

0 ff m+ Δ where fΔ represents the channel bandwidth. If

( )refs t is downconverted respect to f0, it is possible to write the complex envelope of the signal as:


( ) ( )1

2

0

cf

Nj m t

ref mm

s t s t e π−

Δ

=

=

The DVB-T multichannel ambiguity funcsignal ( )refs t can be written as:

2*

1 12 2 (*

0 0

1 12 2 *

0 0

( , ) ( ) ( )

( ) ( )

( ) ( )

d

c cf f

c cf f

j f td ref ref

N Nj m t j p t

m pm p

N Nj p j m t j

m pm p

f s t s t e dt

s t e s t e

e s t e s t e

π

π π τ

π τ π

χ τ τ

τ

τ

+∞

−∞+∞− −

Δ − Δ −

= = −∞+∞− −

Δ Δ −

= = −∞

= − =

= −

= −

Under the following assumptions:

• ( )ms t is a bandwidth-limitedbandwidth equal to 2B)

• the signal bandwidth is always channel bandwidth, 2f BΔ ≥ (i.do not overlap)

• the Doppler frequency is nrespect to the signal bandwidth, f

it is possible to rewrite eq. (3) as:

( )

12 *

0

12

0

( , ) ( ) ( )

,

cf

cf

Nj p

d p pp

Nj p

p dp

f e s t s t e

e AF f

π τ

π τ

χ τ τ

τ

+∞−Δ

= −∞−

Δ

=

= −

=

where ( ),p dAF fτ is the ambiguity functDVB-T channel. Under the realistic assumauto-ambiguity function of a generic single exhibits the same main characteristics, simplified to:

( )

( ) ((

12

0

| ( , ) | | ,

sinc,

sinc

cf

Nj p

d dp

cd c

f

f AF f e

NAF f N

πχ τ τ

ττ

−Δ

=

≈

Δ≈

Δ

From eq.(5) it can be observed that the ranimproved by a factor of cN respect to thechannel usage. Moreover, the ambiguity funone channel represents the envelop of thambiguity function. The number of channevalue of fΔ influence the range resolution alevel.

As a preliminary step, three adjacent Dhave been acquired through a SDR (SoRadio) board, then analysed and processed tof the DVB-T multichannel waveformfrequency is 754 MHz and the whole analysabout 24 MHz of bandwidth (Fig. 1).

(2)

ction (AF) of the

) 2

2 2

d

f d

j f t

j p t j f t

e dt

e dt

τ π

π πΔ

= (3)

d signal (with

smaller than the .e.: the channels

negligible with fd<<2B

2 dj f te dtπ = (4)

tion for a single mption that the DVB-T channel eq.(4) can be

))

|f

f

τ

ττ

=

Δ (5)

nge resolution is e single DVB-T nction relative to he multichannel els cN , and the and the sidelobe

DVB-T channels oftware Defined to obtain the AF

m. The central sed signal shows

Fig. 1 DVB-T mu

In this case, the ambiguitand compared with the onechannel. Plots of the ambig(range) and Doppler frequencworth noting that the rangetimes with respect to the sinDoppler profile maintained th

Fig. 2 Multichannel AF from 3-D view, Range p

The ambiguity function presepeaks due to the known st

ultichannel spectrum

ty function have been computed e obtained for a single DVB-T guity function along time delay cy are represented in Fig. 2. It is e resolution is improved by Ncngle DVB-T channel, while the he same behaviour.

real data (starting from the top):rofile, Doppler profile

ents unwanted deterministic side tructure of the DVB-T signal,


which includes pilots, guard intervals, and the guard band between adjacent channels. In this work a novel signal pre-processing technique based on DVB-T reference signal power spectral density (PSD) [7] is used to reduce these peaks as shown in Fig. 3.

Fig. 3 Multichannel AF from real data 3-D view after reference signal pre-processing

III. EXPERIMENTAL SET UP

The equipment that has been used in this experiment is composed by commercial off-the-shelf low cost TV antennas, two synchronized Ettus USRP2 board equipped with a RF front-end tunable from 800 MHz to 2400 MHz. The main technical specifications of the USRP2 are:

• FPGA Xilinx Spartan 3-2000 EP1C12 Q240C8 “Cyclone”

• 2 High-Speed Analog to Digital Converters (ADCs) operating at 14 bits with a sampling rate of 100 Mega-samples per seconds (100 MS/s)

• 2 High-Speed Digital to Analog Converters (DACs) operating at 16 bit with a sampling rate of 400 MS/s

• Gigabit Ethernet interface

The antenna used during preliminary measurements and the experiment for the target channel is a Yagi-Uda antenna with a receiving gain equal to 18 dB and a Half Power Beam Width of 20 degrees in the horizontal plane. On reference channel, a Yagi-Uda antenna with a gain of 15 dB has been used.

IV. EXPERIMENTAL RESULTS

The experiment scenario geometry is shown in Fig. 4. Specifically, the receiver was located at the Department of Information Engineering in Pisa and the DVB-T transmitter was 14 km away from the receiver at 36° North-East as indicated by the red arrow in Fig. 4. Moreover, the surveillance antenna was pointed at 15° of azimuth and 30° of elevation. The targets of interest were airplanes taking off from the nearby Pisa airport. Fig. 4 shows the trajectory of

the considered target. The expected Doppler frequencies for the target in the surveillance area are shown in Fig. 5.

Fig. 4 Experiment Scenario geometry and target trajectory

Fig. 5 Expected Doppler Frequencies for a target that is moving between the two red dots of Fig. 4 trajectory

Fig. 6 presents a photo of the detected target and summarizes the target main technical information.

British Airways Boeing 737-400 Technical information Values (metres)

Length 36.5 Wingspan 28.9

Height 11.1 Fig. 6 Main technical information of the detected target

The reference and surveillance channels have been simultaneously acquired with the equipment presented in section III. Then the pre-processing technique has been applied and finally the Cross-Ambiguity Function (CAF) relative to three adjacent DVB-T channels has been evaluated (Fig. 7). The peak due to the target echo is clearly visible at the 87th range bin (i.e.: around 1700 m for the geometry considered in Fig. 4).


Fig. 7 CAF of the surveillance area

Moreover, a Doppler frequency value of -169 Hz is in accordance with the expected velocity (more than 450 km/h).

Fig. 8 Target range profile for a single DVB-T channel (blue line on the top) and for three adjacent DVB-T channels (red

line on the bottom)

In order to better evaluate the range resolution improvement, the CAF has been calculated and compared for one and three DVB-T channels. Particularly, Fig. 8 presents the range profile along the Doppler frequency of the target echo (i.e.: -169 Hz). Considering the geometry in Fig. 4, the bistatic range resolution achievable by using a single channel is around 57 m, whereas exploiting three adjacent channels it is around 18 m. It is worth noting as the range resolution relative exploiting three DVB-T channels (red line) is improved respect to the single DVB-T channel case (blue line). As a matter of fact, the blue line range profile shows only one main peak while two peaks are clearly visible on the red line one. This result is actually consistent with the target size reported in Fig. 6.

V. CONCLUSIONS

In this paper, the exploitation of multiple DVB-T channels for a passive radar system has been considered in order to enhance the radar range resolution. A preliminary study has been focused on the ambiguity function analysis in order to verify the suitability of a multichannel DVB-T signal for radar applications. The theoretical study has been supported by preliminary measurements relative to three adjacent DVB-T channels. Experimental results in aerial scenario have been carried out and discussed. As a matter of fact, high resolution DVB-T passive radar can be the first step to perform passive radar imaging and target classification.

REFERENCES

[1] H D. Griffiths and C. J. Baker, “Passive Coherent Location radar systems. Part 1: Performance prediction.” Radar, Sonar and Navigation, IEE Proceedings, vol. 152, no. 3, pp. 124 – 132, 2005.

[2] .C J. Baker, H. D. Griffiths, and I. Papoutsis, “Passive coherent location radar systems. part 2: waveform properties,” Radar, Sonar and Navigation, IEE Proceedings -, vol. 152, no. 3, pp. 160–168, 2005.

[3] P. E. Howland, “Target tracking using television-based bistatic radar,”Radar Sonar and Navigation IEE Proceedings, vol. 146, no. 3, pp. 166–174, june 1999.

[4] Olsen, K.E.; Woodbridge, K.; , "Analysis of the performance of a multiband passive bistatic radar processing scheme," Waveform Diversity and Design Conference (WDD), 2010 International , vol., no., pp.000142-000149, 8-13 Aug. 2010

[5] Bongioanni, C.; Colone, F.; Lombardo, P.; , "Performance analysis of a multi-frequency FM based Passive Bistatic Radar," Radar Conference, 2008. RADAR '08. IEEE , vol., no., pp.1-6, 26-30 May 2008

[6] Conti, M.; Berizzi, F.; Petri, D.; Capria, A.; Martorella, M.; , "High range resolution DVB-T Passive Radar," Radar Conference (EuRAD), 2010 European , vol., no., pp.109-112, Sept. 30 2010-Oct. 1 2010

[7] M. Conti, D. Petri, A. Capria, M. Martorella, F. Berizzi, E. Dalle Mese, "Ambiguity Function Sidelobes Mitigation in Multichannel DVB-T Passive Bistatic Radar", accepted to International Radar Symposium (IRS) 2011, September 7-9, 2011, Leipzig, Germany


Range Only Multistatic Tracking in ClutterDarko Musicki and Taek Lyul Song

Department of Electronic Systems Engineering,Hanyang University, Republic of Korea

[email protected] and [email protected]

Abstract—This paper presents an algorithm for multistatictarget tracking in clutter, using only range difference information(neither bearing nor Doppler information are assumed available).Presence of false tracks, Data association issues as well as the non-linear measurement equation makes this a challenging problem.This paper proposes a solution to this problem by using theGaussian Mixture Measurement likelihood - Integrated TrackSplitting algorithm.

I. INTRODUCTION

1 Multistatic tracking involves using non-collocated transmit-ters and receivers to track the targets. One transmitter canbe paired with one (bistatic) or many receivers (multistaticconfiguration). The receiver measures the time differencebetween the signal propagated directly, and the signal reflectedfrom the target. This setup has many practical benefits, as wellas numerous challenges, and is an active area of research, anice overview can be found in [1].

In this paper we also assume no angle information, whichintroduces a severe measurement non-linearity and also ne-cessitates using more than one stationary receiver (sensor)to ensure system observability. The situation is made morecomplex by uncertain detections and the presence of spuriousmeasurements (clutter). The uncertain presence of target(s)also increases the problem dimensionality.

The signal to clutter ratio is usually low enough so that a sig-nificant number of clutter detections are present in each scan.The tracks are initialized and updated using measurements,thus both true tracks (which follow targets) and false tracks(which do not) are initialized and updated. Furthermore, truetracks may become false, either because the target disappears,or the track may lose a target due to random detections andmeasurement noise phenomena.

A false track discrimination procedure must be used torecognize and eliminate (a vast majority of) false tracks, andrecognize and confirm (a vast majority of) true tracks.

One approach to track information involves using the specu-lar phenomena [2]. At certain target to receiver geometries, thepower of received signal increases sharply. Thus, the detectionthreshold is kept high until the specular detection occurs andthe track is initialized. Then the detection threshold is lowered,but only in the vicinity of established track(s). This approachis innovative, and robust in some environments. However it

1This work was supported by Defense Acquisition Program Administrationand Agency for Defense Development (Republic of Korea) through ProjectADD-09-70-01-03, as collaboration with the University of Melbourne, Aus-tralia under the contract UD090002DD

usually results in significant delays before track is initializiedand, subsequently, confirmed. Additional measures have tobe taken to detect the event of track becoming a false track(“losing its target”).

Here we propose using the Gaussian Mixture Measurementlikelihood - Integrated Track Splitting algorithm (GMM-ITS)algorithm to handle both the false track discrimination andtarget trajectory estimation, in a natural (integrated) fashion.

The GMM-ITS was introduced in [3] as a general algo-rithm for target tracking in clutter, when (severely) non-linearmeasurements are available. It approximates the trajectorystate pdf by a dynamic Gaussian mixture (in the state space).The measurement nonlinearity is handled by projecting themeasurement likelihood from the measurement space into thesurveillance space, where it is then approximated by a Gaus-sian Mixture (summing up to a proportionality coefficient).Trajectory state update is then handled by using the standardKalman filter update operation between every pair of trajectorystate and measurement likelihood Gaussians. To facilitate thefalse track discrimination, the probability of target existenceis recursively calculated in the standard manner [4], [5] usingthe measurement likelihood ratio.

The track confirmation and termination operations use thecalculated probability of target existence as the track qualitymeasure.

This approach offers significant benefits. A standard filteris being used; the only module that is changed between appli-cation is the measurement likelihood projection and approxi-mation [6]–[9]. The atomic operation is the standard KalmanFilter update, which has been studied in every conceivabledetail during the last decades. On the negative side, the numberof operations (and computational requirements) is significantlyhigher than that of a single Kalman filter, but nevertheless itis usually an order of magnitude more frugal than a ParticleFilter implementation [10].

This paper is a natural extension of [11], which limitsuse of GMM-ITS to a trajectory estimation in a multistaticenvironment (no data association environment and associatedfalse track discrimination requirements).

Models and assumptions are presented in Section II, fol-lowed by the description of the measurement likelihood trans-formation in Section III. The GMM-ITS tracker is brieflypresented in Section IV. Simulation results in Section Vvindicate this approach, followed by the concluding remarksin Section VI.


II. MODELS AND ASSUMPTIONS

In this paper we consider two dimensional multistatic surveil-lance scenarios. A single emitter broadcasts signal, which isreceived by multiple sensors. The sensors process the signal,perform the detection and provide detection measurements.All measurements are processed at a fusion center. Here weassume that each sensor receives (takes) measurements simul-taneously, although the algorithm provides a straightforwardextension for asynchronous scanning.

A single target may or may not be present (although thefilter may be extended to the multitarget version).

The sensors also report clutter measurements, which areassumed to follow Poisson distribution with known mea-surement density. Thus, at each scan the tracking algorithmreceives multiple measurements from a number of sensors,without prior knowledge on which measurement is the targetmeasurement.

The usual simplifying assumptions of point targets andthe infinite sensor resolution are used here. The point target(if it exists and follows a trajectory whose only dynamicmodel is known) creates up to one detection per measurementtime per receiver, with probability of detection PD ≤ 1.The infinite resolution sensor assumption translates into “eachmeasurement has only one source; i.e. it is either the targetmeasurement or a clutter measurement, but not both”.

The target existence event χk at time k is modeled as aMarkov Chain [4], [5], where the propagation equals

P{χk} = αP{χk−1}, (1)

where α denotes the Markov transition probability. Eq. (1) isconditioned on the data set used so far.

Standard target trajectory propagation is assumed here

xk = F ∗ xk−1 + ωk (2)

where F is (known) propagation matrix, and ωk is zero meanwhite Gaussian sequence not correlated with any other randomsequence, with (known) correlation matrix Q.

Target trajectory is usually expressed in Cartesian coordi-nates, almost always with position and velocity as elementsof the trajectory state. Here we assume that exists linear pro-jection H from the trajectory state space into the surveillance(position) space, i.e. that Hxk equals the position of the target.

Each sensor receives both the direct signal from emitter, andthe signal reflected by the target. The time difference betweenthese signals is a measurement of the difference in two lengthsthe signal had to travel. Each sensor corrupts measurementwith additive measurement noise, and the target measurementfrom sensor (s) at time k equals

y(s)k = h

(xk;x

(s))+ ν

(s)k (3)

with xk denoting the trajectory state, x(s) denoting sensortrajectory state, and ν

(s)k denoting a sample of zero mean,

white Gaussian process with standard deviation σ(s). Themeasurement function is

h(xk;x

(s))= a

(s)k − b(s) (4)

where a(s)k denotes the total distance from the emitter to

target to sensor (s), and b(s) denotes the direct distance fromsensor (s) to the emitter. Both the (stationary) emitter and the(stationary) sensor positions are assumed known.

Disregarding the measurement noise and given the mea-surement value of h(xk;x

(s)), and therefore the value a(s)k ,

possible target positions form an ellipse where the emitter andsensor (s) form the focal points, and with the major axis ofa(s)k , and the minor axis of√(

a(s)k

)2

−(b(s)

)2

. (5)

Due to the measurement noise, the line of this “target positionuncertainty ellipse” becomes wide. This measurement equationis non-linear.

In addition to the signal reflected by the target (and de-tected with the probability of detection PD, each sensoralso receives clutter (plus amplification noise) signal, whichresults in spurious detections termed the clutter measurements.The clutter measurements are assumed to follow a Poissondistribution parameterized by the clutter measurement densityρ in the measurement space. The clutter measurement densityis assumed a priori known, it can be estimated otherwise [12].

Thus, at measurement time k, receiver i receives a setof measurements Yk(i) with random cardinality mi

k; whereYk,j(i) denotes the jth measurement received by receiver i

at time k. The fusion center receives measurement sets fromall receivers, Yk = {Yk(1), . . .Yk(R)} where R denotesthe number of receivers. Denote by Yk the sequence of allmeasurement sets up to and including time k.

III. MEASUREMENT LIKELIHOOD TRANSFORMATION

The likelihoods of measurement Yk,j(i) is approximated by aGaussian Mixture in the surveillance space:

p(Yk,j(i)|xk) ≈ cjk,i

G∑g=1

γj,gk,iN

(zj,gk,i;Hxk,R

j,gk,i

)(6)

where N (x;m,P) denotes the Gaussian pdf of variable x

with mean m and covariance P. The value of constant cjk,i isimportant for data association and equals

cjk,i =

A±σ(s)

2σ(s), (7)

where A±σ(s) denotes the area between ±σ(s) ellipses. Treat-ing the Gaussian Mixture components as mutually exclusiveand exhaustive events, we obtain

G∑g=1

γj,gk,i = 1, γ

j,gk,i > 0 (8)

constraint.To obtain the Gaussian Mixture presentation of measure-

ment likelihood in the surveillance space, the area between±σ(s) ellipses is divided into component ellipses, where eachcomponent ellipse is a footprint of one measurement Gaussian


0 2000 4000 6000 8000 10000−5000

0

5000

receiver 2

receiver 3

receiver 1

target

emitter

Figure 1. Simulation scenario.

Mixture component, as presented on Figure 1. The process ofobtaining (6) are presented in [11].

The likelihood of each measurement received by eachreceiver is converted into a (separate) Gaussian Mixture.

IV. GMM-ITS

Track state at time k is hybrid, as it consists of a binary randomvariable (target existence) denoted by χk and a continuousrandom variable (trajectory state) xk:

P{χk,xk} = P{χk}p(xk|χk), (9)

where trajectory state xk is only defined given the target exis-tence event χk. Trajectory state pdf is initialized and updatedby nonlinear measurement(s). Therefore, the trajectory statepdf is non-Gaussian, and is approximated here by a GaussianMixture; for example prior pdf at time k approximation is

p(xk|χk,Y

k−1)= (10)

Ck∑c=1

ξc N(xk; x

(c)k|k−1,P

(c)k|k−1

)

where c denotes track component, or index of the trackcomponent, depending on the context, and

Ck∑c=1

ξc = 1, ξc > 0. (11)

The advantage of using Gaussian Mixtures for both mea-surement likelihood presentation and for the track state pdfpresentation, is that we may replace non-linear operations bya number of simple Kalman filter updates.

The GMM-ITS recursion cycle at time k starts withthe posterior state at time k − 1, P{χk−1|Yk−1} andp(xk−1|χk−1,Y

k−1) and consists of

• track prediction,• measurement component selection and likelihood calcu-

lation,• track update,

• track component management, and• track output.

The measurement component selection and likelihood calcu-lation, the track update and track component managementare performed sequentially on each measurement set Yk(i),where the updated track state using the measurement setsYk(1) . . .Yk(i−1) serves as the prior track state with respectto the measurement set Yk(i) update. Thus, for the rest of thissection, due care has to be exercised when using P{χk|Yk−1},Ck, ξc ,x(c)

k|k−1 and P(c)k|k−1, as they are the propagated values

only when using Yk(1), otherwise they are the track state afterYk(1) . . .Yk(i− 1) have been applied.

The probability of target existence serves as the track qualitymeasure and is used for false track discrimination. When (andif) the probability of target existence rises above a predeter-mined confirmation threshold, the track is assumed true, isconfirmed, and the information is forwarded to the operatorsor the higher level of information fusion. When the probabilityof target existence falls below a predetermined terminationthreshold, the track is declared false and terminated.

A. Track Prediction

Track prediction propagates track state pdf from time k−1 totime k,

p(xk|Y

k−1)=

Ck∑c=1

ξc N(xk; x

(c)k|k−1,P

(c)k|k−1

)(12)

Relative probability ξc of each component c does not changewhen propagating and each track component propagates indi-vidually as per standard Kalman Filter prediction,and[

x(c)k|k−1,P

(c)k|k−1

]= (13)

KFP

(x(c)k−1|k−1,P

(c)k−1|k−1,F,Q

).

The probability of target existence propagates by

P{χk|Yk−1} = αP{χk−1|Y

k−1} (14)

B. Selection and Likelihood Calculation

Each track component c at time k selects a subset of mea-surement components for update. The selection procedure willnot be detailed here, suffice it to say that it is described inmany references [13]–[15], and will not be detailed here. Eachcomponent selects measurement components with significantmutual likelihoods.

The likelihood of a selected measurement Yk,j(i) compo-nent g, with respect to the track component c is

pj,g,ck,i =

cjk,i

PG

N(zj,gk,i;Hx

(c)k|k−1,S

j,g,ck,i

)(15)

where PG is the gating probability and

Sj,g,ck,i = HP

(c)k|k−1H

T +Rj,gk,i. (16)

The likelihood of a measurement Yk,j(i) component g whichis not selected by the track component c, and with respect


to the track component c is pj,g,ck,i = 0. The likelihood of

measurement Yk,j(i) with respect to track component c is

pj,ck,i =

G∑g=1

γj,gk,ip

j,g,ck,i , (17)

and the likelihood of measurement Yk,j(i) with respect to trackis

pjk,i =

Ck∑c=1

ξc pj,ck,i. (18)

C. Track Update

Track measurement likelihood ratio of measurement set Yk(i)is

λk,i = 1− PDPG + PDPG

mi

k∑j=1

pjk,i

ρ. (19)

Updated probability of target existence equals

P{χk|Yk} =

λk,iP{χk|Yk−1}

1− (1− λk,i)P{χk|Yk−1}. (20)

During the track update, each existing track componentis replaced by a number of new track components. Eachpair of track component c and a measurement component g

(of measurement Yk,j(i) forms a new component, given thattrack component c selects measurement component g. Denoteby c+ new component formed by track component c andmeasurement component g, then the updated track state pdf isapproximated by

p(xk|Y

k)=

Ck+1∑c+=1

ξc+N(xk; x

(c+)k|k ,P

(c+)k|k

)(21)

where relative component probability ξc+ is

ξc+ = PDPG

ξcγg

λk,i

pj,g,ck,i

ρ, (22)

or, when the component is associated with the null measure-ment

ξc+ = ξc1− PDPG

λk,i

. (23)

which satisfy constraint

Ck+1∑c+

ξc+ = 1. (24)

Mean x(c+)k|k and covariance P

(c+)k|k of new component c+ are

obtained by simple Kalman filter update of track componentc by measurement component g[

x(c+)k|k ,P

(c+)k|k

]= (25)

KFU

(x(c)k|k−1,P

(c)k|k−1, z

j,gk,i,R

j,gk,i,H

),

where KFU denotes the Kalman filter update operation. Forthe association with the null measurement,[

x(c+)k|k ,P

(c+)k|k

]=

[x(c)k|k−1,P

(c)k|k−1

]. (26)

D. Track Component Management

As presented in this section, the number of track compo-nents grows exponentially in time. To prevent the saturationof available computational resources, some track componentmanagement has to be implemented [16]. Proposed algorithmis an instance of track splitting algorithm [3], [5], [17], [18],and all track splitting track component management methodscan also be used here.

These techniques include track component pruning, wherecomponents with small relative probabilities ξ are removed,track component subtree pruning, where whole subtrees ofcomponents are removed based on track component relativeprobabilities [13]. Finally, one can merge “close” track com-ponents, where definition of “close” differs between variousproposals [19]–[21].

E. Track Output

As the track trajectory estimate, the authors use the trajectorystate mean xk|k and covariance Pk|k, defined as the mean andcovariance of a posteriori track state pdf at time k

xk|k =∑c+

ξc+ x(c+)k|k (27)

Pk|k =∑c+

ξc+

(P

(c+)k|k + x

(c+)k|k

(x(c+)k|k

)T)− (28)

xk|kxTk|k

V. SIMULATIONS

Simulations have been used to verify proposed algorithm. Asimple scenario is shown in Figure 1. Three sensors generatemultistatic range only target measurements with the probabil-ity of detection PD = 0.9. At each scan each sensor generatesa random number of clutter measurements, on the average eachsensor generates 3 clutter measurements per scan.

Multistatic measurements are taken once per 10 seconds,and total simulated time is 1000 s. Each simulation scenariois repeated 500 times with independent measurements.

0 200 400 600 800 10000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

time

conf

irmed

true

Figure 2. True track confirmation rate


Tracks are initialized using all measurements, and thetracks are propagated as proposed in this paper. False trackdiscrimination has been applied using the probability of targetexistence as the track quality measure. No false tracks wereconfirmed, and the true track success rate is depicted onFigure 2.

Tracking results depend on the geometry of the situationwhich is the current research area. Another area is the adaptivechoice of sensors to be used, the resource allocation problem.

VI. CONCLUSIONS

This paper illustrates use of the GMM-ITS algorithm whentracking a single target in clutter, using the highly-nonlinearmultistatic measurements without bearings information.

The initial results are highly encouraging, and the researchcontinues in various practical aspects of the application.

REFERENCES

[1] S. Coraluppi, “Multistatic sonar localization,” IEEE Journ. OceanicEngineering, vol. 31, no. 4, October 2006.

[2] D. Grimmett, “Multistatic target tracking using specular cue initiationand directed data re-trieval,” in 11th International Conference on Infor-mation Fusion, Fusion 2008, Cologne, Germany, July 2008.

[3] D. Musicki and R. Evans, “Measurement Gaussian sum mixture tar-get tracking,” in 9th International Conference on Information Fusion,Fusion 2006, Florence, Italy, July 2006.

[4] D. Musicki, R. Evans, and S. Stankovic, “Integrated Probabilistic DataAssociation (IPDA),” IEEE Trans. Automatic Control, vol. 39, no. 6, pp.1237–1241, Jun 1994.

[5] D. Musicki, B. La Scala, and R. Evans, “The Integrated Track Splittingfilter - efficient multi-scan single target tracking in clutter,” IEEE Trans.Aerospace Electronic Systems, vol. 43, no. 4, pp. 1409–1425, October2007.

[6] D. Musicki, “Bearings only multi-sensor maneuvering target tracking,”Systems Control Letters, vol. 57, no. 3, pp. 216–221, March 2008.

[7] ——, “Multi-target tracking using multiple passive bearings-only asyn-chronous sensors,” IEEE Trans. Aerospace and Electronic Systems,vol. 44, no. 3, pp. 1151–1160, July 2008.

[8] ——, “Bearings only single-sensor target tracking using Gaussian mix-tures,” Automatica, vol. 45, no. 9, pp. 2088–2092, September 2009.

[9] D. Musicki, R. Kaune, and W. Koch, “Mobile emitter geolocationusing TDOA and FDOA measurements,” IEEE Trans. Signal Processing,vol. 58, no. 3, pp. 1863–1874, Mar 2010.

[10] B. Ristic, S. Arulampalam, and N. Gordon, Beyond the Kalman Filter.Artech House, 2004.

[11] T. L. Song and D. Musicki, “Range only multistatic tracking,” in13th International Conference on Information Fusion, Fusion 2010,Edinburgh, UK, July 26-29 2010.

[12] ——, “Adaptive clutter measurement density estimation for improvedtarget tracking,” IEEE Trans. Aerospace Electronic Systems, vol. 47,no. 2, pp. 1457–1466, April 2011.

[13] S. Blackman and R. Popoli, Design and Analysis of Modern TrackingSystems. Artech House, 1999.

[14] Y. Bar-Shalom and E. Tse, “Tracking in a cluttered environment withProbabilistic Data Association,” Automatica, vol. 11, pp. 451–460, Sep1975.

[15] Y. Bar-Shalom, K. Chang, and H. Blom, Multitarget Multisensor Track-ing. Artech House, 1990, vol. 1, ch. Automatic track formation inclutter with a recursive algorithm, pp. 25–42.

[16] S. Blackman, Multiple-target tracking with radar applications. ArtechHouse, 1986.

[17] T. Kurien, Multitarget Multisensor Tracking, Y. Bar-Shalom, Ed. ArtechHouse, 1990, vol. 1.

[18] D. B. Reid, “An algorithm for tracking multiple targets,” IEEE Trans.Automatic Control, vol. 24, no. 6, pp. 843–854, Jun 1979.

[19] R. A. Singer, R. G. Sea, and K. Housewright, “Derivation and evaluationof improved tracking filters for use in dense multi-target environments,”IEEE Trans. Information Theory, vol. 20, no. 4, pp. 423–432, Jul 1974.

[20] D. J. Salmond, “Mixture reduction algorithms for target tracking inclutter,” in SPIE: Signal and Data Processing of Small Targets, vol.1305, Orlando, Florida, April 1990, pp. 434–445.

[21] J. L. Williams and P. S. Mayback, “Cost-function-based gaussian mix-ture reduction for target tracking,” in 6th International Conference onInformation Fusion, Fusion 2003, Cairns, Queensland, Australia, July2003, pp. 1047–1054.


Tracker Quality Monitoring by Non Dedicated Calibration Flights


The Transponder Data Recorder: first implementation and applications


ADS B/MLAT surveillance system from High Altitude Platform Systems


Space based ADS B A small step for technology a giant leap for ATM?



Tracker Quality Monitoring by Non-Dedicated Calibration Flights

Matthias Hess, Ralf HeidgerTM/SP

Deutsche Flugsicherung GmbH (DFS)Langen, GERMANY

{matthias.hess | ralf.heidger} @ dfs.de

Jochen BredemeyerFCS Flight Calibration Service GmbH

Braunschweig, GERMANYbrd @ flightcalibration.de

Abstract Sensor and tracking quality analysis is a key factor in the quality assurance in surveillance for Air Traffic Control(ATC) at an Air Navigation Service Provider (ANSP) like the DFS. In this paper we present a collaboration infrastructure that allows automatic tracker quality analysis by using results of regular ferry and mission flights conducted for inspection of navigation aids. The goal of this infrastructure is to allow the two companies Flight Calibration Service GmbH (FCS) and Deutsche Flugsicherung GmbH (DFS) to combine their respective expertise in an efficient manner.

Flight Calibration, Tracker Evaluation

I. INTRODUCTION

The quality of a tracking system can be estimated in several ways which should be combined for a thorough analysis:

Evaluation against synthetically created scenarios

Evaluation against reconstructed trajectories

Evaluation against calibrations flights

When employing synthetic scenarios, one generates artificial sensor data on which the tracker operates. Usually the true trajectory is perfectly known, but the simulated sensor data do not necessarily reflect real behavior. The tracker is evaluated against this simulated trajectory. At DFS the AFS simulator [1], [12] is such a tool to create tracker testing scenarios.

In order to evaluate the tracker against traffic of opportunity, an offline approach is quite common. A trajectory is reconstructed from recorded real world sensor data with algorithms, that are potentially more precise but take more time than online tracking algorithms ([5] or [6], for instance), which prohibits their use in a tracking system. This approach is taken by the well-established SASS-C program developed by EUROCONTROL [3].

Derived from the second method is the so called quasi-online quality control used in a program suite developed by DFS [10], [2]. Here a less time consuming algorithm is used for the reconstruction that allows tracker quality analysis in a timely fashion rather than the offline method of SASS-C.

A third and more expensive method is using calibration flights. These give a reference trajectory of unparalleled

precision in an opportunity traffic scenario, but have spatial and temporal limits.

All these approaches are deployed at DFS but the focus is put on the third approach.

Radar flight inspection for civil facilities is nowadays usually not performed periodically, there may be only a single flight inspection after deployment. In contrast to dedicated radar flight trials as depicted in ICAO DOC 8071 (Part III) [1],this new approach makes use of the position data collected during ferry and mission flights performed for flight inspection of terrestrial navigation aids. This is a task to be performed on a regular basis according to ICAO DOC 8071 (Part I) [1].

II. OVERVIEW

In order to accomplish a flight calibration data based tracker evaluation, several steps are required:

Conducting a calibration flight

Preprocessing the collected raw data

Collecting corresponding tracker data

Analyzing tracker data with respect to the flight path data

The key for the kind of tracker evaluations that we will describe in this paper is bringing together the calibration flight position data and the tracker data. The former is provided by FCS as a by-product of their calibration flights and tracker data is recorded at DFS. As DFS is doing the tracker evaluation and has the respective data already ready at their hands, a data exchange server between FCS and DFS was setup to provide DFS with the flight path data.

The different stages are schematically presented in figure 1.


Figure 1. Principal data flow in a flight calibration data based tracker analysis.

In section III we focus on the calibration flights and the processing of the resulting data, i.e. steps 1 and 2. In section IVwe describe in a more detailed fashion the tracker evaluation. Finally, to show how we employ the presented infrastructure,we present an evaluation based on opportunity traffic with corresponding calibration flight data in section V.

III. CALIBRATION FLIGHTS

Under normal operations, a single FCS flight inspection aircraft spends more than 25 hours a week on ferry and mission flights. This mission requires that the flight inspection system (FIS) has a reference position estimate significantly more accurate than that of the facility under inspection. The implemented position estimation technique integrates differential global navigation satellite systems (DGNSS, space-based, ground-based correction) and an inertial navigation system (INS). A total uncertainty in positioning of < 1 m in the horizontal plane is normally achieved.

Furthermore, the attitude vector (roll, pitch, bank angle) is available which helps to identify critical turning maneuvers. These potentially block line-of-sight transmissions from the

to the ground sensor, resulting in a degraded slant range or monopulse estimation of a certain beam dwell.

Position data is generated and recorded always when airborne, so there is a huge dataset of 25 flight hours available by the end of the week when one crew returns to their home base.

Flight inspection missions of the two FCS aircraft cover, among others, dense areas as Frankfurt, Munich, Vienna and Zurich terminal manoeuvering area (TMA) within coverage of at least one Airport Surveillance Radar (ASR) and two mid-range radars (SREM).

The mission traffic serves as a valuable source to obtain the necessary data basis to check the radar tracker results against a reliable, known target. There is no ordinary traffic used but two

dedicated targets having a high precision position vector with timestamps against the GPS second-of-week.

The two FCS measurement aircraft with callsigns D-CFMD and D-CFME can be easily identified through

their Mode S technical addresses which is delivered from a Mode S sensor monoradar service message (ASTERIX CAT034).

A continuous validation of the multi-sensor tracker results allows a long-term quality assurance with no additional flight costs incurred.

IV. TRACKER QUALITY ANALYSIS

As mentioned above there are several ways to measure the quality of a tracking system - once one has defined the respective measures. For the purposes of this paper tracking quality can roughly be divided into two main areas, track detection and tracking accuracy. A track detection analysis (TDA) tries to measure how well the tracker finds real targets and can distinguish them from false ones. It also answers questions about the ability of the tracker to follow the target throughout its flight.

On the other hand, a track accuracy analysis (TAA) is concerned with the precision of the tracker output, i.e. how closely the tracker follows a target.

We use calibration flights for tracker quality analysis for the purposes of this paper. Here the reference trajectory is given by high-precision FIS data collected aboard a calibration aircraft. These data are usually much more precise than ordinary sensor data. Hence, the basis for tracker evaluation is much more sound. The drawbacks of this method are the costs for calibration flights and their restricted data set, spatially as well as temporally. These flights can cover a certain area of interest and for a limited period of time only.

But for very exact tracker evaluation there is no other reference data being more accurate: These data are obtained in a real-world scenario (rather than a synthetic scenario) and have unique precision.

A. Tracking QualityTracking quality can be roughly divided into two domains,

TDA and TAA. The former indicates the ability of a tracking system to reliably detect any real target and to distinguish it from erroneous sensor signals. The latter measures the accuracy with which a system follows real targets.

Commonly used measures for TAA are deviations of the tracking signal from the true trajectory across and along its path.

B. Accuracy AnalysisTwo of the most commonly used quality measures for

tracking systems are the across trajectory and along trajectory deviations. Since it is of interest to have as few quality measures as necessary, one combines the deviations during the time of the trajectory into a single root mean square (rms)value.

Calibration Flight

Flight Position Data

Data ExchangeServer

Track Data

TrackerAnalysis

FCS

DFS


If we have a quantity that depends on time then the rms value of that quantity is defined as follows:

where and are the start and end time over which is defined and denotes the duration of . For our analysis we use the across and along deviations, and ,respectively. Trajectories can be modeled as mappings

from time into state space that is spanned by position , velocity and acceleration .

Here we neglect the acceleration and consider the velocity to be equal to the first time derivative of the position

. So we can reduce a trajectory to a mapping from time to position:

and we can set

So we obtain for and the distances across and along two trajectories and the following expressions:

and

where denotes the Euclidean norm of a quantity .And finally, we define and to be the rms values of

and respectively. For data in the WGS84 frame [13], the Euclidean distance must be replaced by a numeric approximation of the true distance on the spheroid.

V. LIVE EXAMPLE

This section finally shows how the flight calibration and the tracker evaluation work together. We present how the chain links from collecting flight position data to an actual tracker evaluation work together.

For the tracker evaluation we use the Analysis Working Position (AWP) developed at DFS [10] together with an extension of the Batch Estimator (BE), an analysis module for the AWP that was presented in an earlier paper [2]. Figure 2shows the course of one of the calibration flights that was used in our analysis within the AWP. The data of that flight were collected for a different purpose but as a by-product they could be used for a tracker evaluation.

Figure 2. Data of an actual calibration flight shown in the AWP that is used for analysis purposes.

We show in figure 3 a detailed comparison of the calibration flight data and the actual tracking output. Most of the time the tracker does not deviate significantly from the reference trajectory.

Figure 3. Comparison of the tracker output (green dots) and the reference trajectory obtained from the calibration flight. The indicated deviation is about

0.13 nm.

As described in section II there are two different data sources that are combined together for the final evaluation: Data recorded during a calibration flight and sensor and tracking data of an ATC system that covers the time andlocation frame spanned by the calibration flight, i.e that contains data for the whole calibration flight.

In order to demonstrate the potential of our analysis system, we have chosen not to take data from an operational tracking system, but from a system that is not tuned optimally. Additionally we do not use all sensor data that is available.This forces the tracker to produce errors that can be detected by our automated evaluation.

A. Data CollectionThe calibration flight data is collected on-board during the

flight. The data is processed and put on a server. At DFS this


server is checked regularly for data updates and newly arrived data are downloaded and processed.

If a new data set is available, the corresponding sensor and tracking data are retrieved from an archive at DFS. These data are then merged together to form the basis of the tracker evaluation.

B. Data AssociationThe time bases of these data will be different. The

calibration flight is based on GPS time and the ATC system is fixed to UTC. So these time bases have to be adjusted as a first step in merging them together. The next step is data association. Data of the calibration flight must be correlated with the corresponding tracker output. Only then is an evaluation meaningful.

So far we use only a few correlation steps: Firstly, we identify the 24 bit Mode S address of the calibration aircraft.This data is unique and stored in the position data file. From the tracker recording we extract the corresponding track numbers, if sensors with Mode S capabilities are available and cover the calibration flight. Then data from regions without Mode S coverage are correlated based on these track numbers. There can be several track numbers for the calibration flight if the tracker fails to detect a continuous track. In a final step we correlate tracking data to the reference trajectory by statistical and geometric measures.

The calibration data contain, amongst other, the standard deviation of the position and velocity. So it is possible to use statistical distance measures if the tracker output contains the specific data, too. This is not guaranteed because EUROCONTROLs ASTERIX standard does not require these data to be present in tracker output. If these data are not available we use simple geometric distances for correlation.

C. Automated AnalysisThe associated data obtained in the association step is used

to calculate several measures that provide indication of the quality of the tracker. Ideally the quality would be indicated by one or few numbers only, like the rms of across and along distances. For a thorough understanding of the tracking behavior this is usually not sufficient, so we calculate more values in our automated analysis.

Our TAA analysis for this calibration flight gives an overall rms value for the deviations of 333.6 m across and 315.9 malong, respectively. Although not very good for operational systems these values are acceptable. Surprisingly the value for the along deviation is about the same as the value for the across deviation. One would expect this to be much smaller as large along deviations usually indicate a problem related to time. So further investigations into the reasons for this behavior is required.

There is another automatically calculated quantity at our disposal, the histogram of deviations across and along. Figure 4shows those histograms. As expected there are many small deviations, indicating that the tracker usually performs well, but there are too many large deviations which add to the rms distance values.

Figure 4. Non-normalized histogram of deviations across and along. We have chosen a logarithmic scale in order to see the small frequencies properly.

When we plot the deviations over the trajectory time (figure 5) it is easily seen that there are few spots with very large deviations. This gives hints for manual inspection.

Figure 5. Deviation plotted over the time of the trajectory. Remarkably, there are a few spots with very large deviations. These are caused by track drops and the fact that the tracks are interpolated in the calculation of rms

values.

Manual AnalysisAt first glance an automated analysis is a good indicator for

the tracker working properly. So if, for instance, the rms values are always well below a certain level, no further analysis would have been required. But in case of unexpected peaks, anautomated analysis can give valuable hints but cannot in general obtain the reasons for erroneous behaviour of the tracker. So those cases require a manual intervention.

The automated analysis already identified hot spots in the deviation. An indication of why the tracker shows these large deviations can be obtained through figure 6. Here the correlation between flight altitude and deviation is shown. For low altitudes the tracker shows large deviations.


Figure 6. Correlation between altitude and devaition. For low altitudes the deviation becomes excessively large.

Through the time of day one can identify the situation that caused these deviations:

Figure 7. Tracker output vs reference trajectory at the time of day where there are the hottest spots. This figure shows a part of the calibration flight close to an airport. The largest deviations are found near the airport.

Obviously the tracker drops the track and continues it at another location (figure 7). This is usually caused by an insufficient sensor plot supply. So for certain parts there are no tracks. Note again, that these errors have been induced deliberately and do not reflect the operational performance of the tracker.

The lack of sensor plots for that area is confirmed by inspection of the corresponding data set. So we are able to explain the tracking behavior. The airport is not covered adequately by the chosen sensor set.

VI. CONCLUSION AND OUTLOOK

In this paper we have presented a method that automatically combines data from calibration flights with data from ATC sensors and tracking systems in order to conduct a tracker evaluation. We have shown the feasibility of the presented

approach with real data from both sources, the calibration flight data and sensor data from opportunity traffic. In order to force the tracker to show erroneous behavior, we have inhibited some sensors and mistuned the tracker. We then showed a detailed analysis that identified some of the problems that lead to the erroneous tracker behavior.

From that analysis the limitations of automated tracker tests become obvious. The results of such tests should not be taken for granted, even if the quality numbers indicate proper work.To find the cause of erroneous behavior, automated tests can be helpful in providing hints on where to look more thoroughly.

A combinatorial analysis of different diagrams from an automated test, e.g. the deviation and altitude along the time of trajectory, is very valuable in identifying poor performance.

The next version of SASS-C (version 7) is supposed to handle comparisons between different tracking sources, too. We installed that version and converted the calibration flight data to ASTERIX CAT062, as the exchange format for tracking output. Effectively we created a second tracker from the calibration flight data and used that in SASS-C as reference trajectory. We then tried to analyze the above situation with SASS-C. Unfortunately, we were not able to use the whole data set of one and a half hour of German sensor data to obtain meaningful results. The reason is probably the still-beta status of that SASS-C version.

Our future development aims at providing and implementing more statistical measures, that furthers facilitates a complete analysis of tracking behavior. We have seen that correlations of basic measures (like the aforementioned deviation and altitude over trajectory time) play an important role in such an analysis. We will transfer these ideas to thebatch estimator where the calibration flight reference trajectoryis replaced by reconstructed trajectories. We will also focus on the TDA which has been left out in this paper completely.

We also hope that further manual analysis of our tracker will result in more experience to improve the algorithms which then lead to a higher degree of automation in tracker evaluations.

REFERENCES

[1] ICAO Document 8071 - Manual on Testing of Radio Navigation Aids, Volume III (Testing of Surveillance Radar Systems), First Edition 1998.

[2] Matthias Heß, Ralf Heidger, "Trajectory Reconstruction for OTQC in the Phoenix Analysis Working Position", in Enhanced Surveillance of Aircraft and Vehicles (ESAV) Proceedings (2010), Berlin, Germany.

[3] - SASS-C-UM-MAN-30,ed. 1.90, Eurocontrol, Brussels, 2010

[4] Jesús García, Juan A. Besada, Andrés Soto and Gonzalo de Miguel:Opportunity Trajectory Reconstruction Techniques for Evaluation of

ATC Systems , International Journal of Microwave and Wireless Technologies (2009), 1 : 231-238.

[5] Jesús García, Andres Soto, Gonzalo de Miguel, Juan Besada, Paula Tarrio: Trajectory reconstruction techniques for evaluation of ATC systems , in Enhanced Surveillance of Aircraft and Vehicles (ESAV) Proceedings (2008), Island of CAPRI, Italy, 198-203.

[6] Juan Besada, Gonzalo de Miguel, Andrés Soto, Ana Bernardos: Algorithms for Opportunity Target Reconstruction , in Enhanced

Surveillance of Aircraft and Vehicles (ESAV) Proceedings (2008), Island of CAPRI, Italy, 212-217.


[7] Radoslav Natchev, Ralf Heidger: Trajectory computation for tracker evaluation and linkage processing , in Enhanced Surveillance of Aircraft and Vehicles (ESAV) Proceedings (2008), Island of CAPRI, Italy, 192-197.

[8] Ralf Heidger, Kai Engels: An Infrastructure for Online Tracking Quality Control , in Enhanced Surveillance of Aircraft and Vehicles (ESAV) Proceedings (2008), Island of CAPRI, Italy, 218-224

[9] Ralf Heidger: The PHOENIX White Paper. V. 3.0. DFS Langen 2011.

[10] Ralf Heidger, Ha Son Nguyen: An analysis working position for radar data processing quality control in Enhanced Surveillance of Aircraft and Vehicles (ESAV) Proceedings (2007), Bonn, Germany.

[11] Ralf Heidger, Thomas Klenner, Roland Mallwitz: The PHOENIX Multi-Radar Tracker System for Air Traffic Control Applications , in: Air Traffic Control Quarterly. Vol. 12, Number 3, 2004, pp. 193-222.

[12] Roland Mallwitz: DFS Approach on Tracking System Performance Analysis to determine ATC separation minima in International Radar Symposium (IRS 2005), Conference Proceedings, DGON, Bonn,Germany.

[13] Department of Defense World Geodetic System 1984, Its Definition and Relationships With Local Geodetic Systems, NIMA Technical Report TR8350.2, Third Edition, 4 July 1997


The Transponder Data Recorder: first implementation and applications

G. Galati1, M. Leonardi1, E. G. Piracci1, N. Petrochilos2, S. Samanta3

1DISP and Vito Volterra Centre Tor Vergata University, Via del Politecnico, 1 00133 Rome, Italy

{galati, leonardi, piracci }@disp.uniroma2.it 2 University of Reims, France

3B. Tech, Electronics and Communication Engineering National Institute of Technology, Durgapur, India

[email protected]

Abstract—The Transponder Data Recorder is an experimental 1090 MHz signal acquisition system designed by the Radar and Navigation group at Tor Vergata University to record the signals in the Secondary Surveillance Radar band, centered at 1090 MHz. The peculiarity of the receiver is that it is based on five receiving chains (4 linear chains with large dynamic range and one with a logarithmic receiver) connected to a wideband linear array antenna. The TDR was developed in order to analyze the channel traffic and to test the new signal processing algorithms, in the research frame on multilateration (MLAT) and Automatic Dependent Surveillance (ADS-B), with real signals.

Keywords- Mode S, Multilateration, signal processing

I. INTRODUCTION

Today the 1090 MHz channel, exploited first by the military IFF (Identification Friend or Foe) systems and then by the secondary surveillance radar (SSR) [1], is widely used for air traffic (but also in airport for vehicular traffic) surveillance. In this context there are many applications that use the 1090 MHz signals. Some of these, such as ADS-B and Multilateration, are becoming increasingly important within the air traffic control, and may integrate or, in some cases, replace the SSR radar stations. For these reasons the integrity and the efficiency of these systems have become very important. In a typical high-density airspace, an increasing number of transponders (airborne or vehicular) transmit signals at 1090 MHz, either as replies to the SSR stations (conventional and Mode S), or spontaneously ('Squitter'). Also in the future the burden of the channel may be increased by the TIS-B stations, which provide information on non-ADS-B aircraft using Mode S signals. In order to reduce the effects of receiving superimposed signals from different sources, we studied signal processing algorithms, useful to discriminate and separate overlapping sources; some of these algorithms need a multichannel receiver and an antenna array [2]. Hence the need for a 1090 MHz signals acquisition system with appropriate characteristics, useful to evaluate the efficiency of the separation algorithms using the received signals, and also to compute traffic analysis and statistics. This paper presents a description of this system, called TDR (Transponder Data Recorder), complying with ICAO and RTCA requirements [3],[4]. It has been designed and developed by the Radar and

Navigation group, RadarLab, at Tor Vergata University. Based on the RadarLab requirements, the array antenna has been designed, realized and tested by the Microwave Laboratory at the University of Calabria, Rende (CS), Italy. The design of the antenna, the analogue front-end and the digital section as well as the results from the first use of TDR, with the analysis of the 1090 MHz channel around the experimental area (i.e. the Tor Vergata University area) are presented. Moreover we analyze the signals density and present the statistics of each signals type (conventional, Mode S), and finally the statistics of overlapping signals. Finally, we present the results of tests of the preliminary application of the separation algorithms on the recorded signals and the proposal for future work and conclusions.

II. TDR DESIGN

The research requirements have driven the design of the TDR. In order to use decoding algorithms based on array processing [6], the selected type of the antenna is a uniform linear array, with 6 elements. The analog part is composed by four receiving chains, connected to the four central elements of the array, and one logarithmic receiver connected to a side element of the array (the other side element is connected to a 50 Ohm load). The logarithmic channel is to be used for (a) Reply detection, (b) Evaluation of compliance of the pulse. The digital section must sample the channels with a shared clock, and it has to reach high sampling rate (up to 100 MS/s) to perform the better phase estimation between the channels.

A. Antenna

Figure 1. Photograph of the six elements array

The antenna was developed by Università della Calabria, Microwave Lab [5]. It is a six patch elements on a stratified dielectric support. A half wavelength spacing between the


elements and a linear vertical polarization have been chosen. The antenna has a pattern of each array element wide enough in both directions in order to cover the air traffic and the surface traffic, and it is possible to obtain a bandwidth of 30 MHz, needed to maintain the fidelity in signal analysis.

B. Analog front-end The analog section has a dynamic range of 70 dB , figure

3 shows a schematic of the TDR analog section with the four linear receiving chains and the logarithmic receiver. The low-noise amplifier (LNA) permits a good noise figure (0,8 dB) and a total gain of 60 dB. The RF filters are Surface Acoustic Wave type (SAW), with a bandpass of 50 MHz. The IF (Intermediate Frequency) is 21.5 MHz and the output of the IF section is filtered with a band-pass filter and a DC-block. The variable attenuator (in steps from 0 dB to 16 dB) is useful to shift the dynamic range in order to use the TDR in an aeroportual area, or in a wide area.

Figure 2. TDR blcok scheme

Figure 3. linear and LOG channel - analog section

The signal at 1065 MHz, used for the frequency down-conversion in the mixer, is generated by a PLL that use a 10 MHz clock as a reference. The reference clock is obtained by an internal quartz oscillator, otherwise it should be taken by the digital section, obtaining a clock sharing between the analog and digital section. The logarithmic receiver has an RF input linear dynamic range of 60 dB, and it is preceded, as for the linear chains, by the filtering and LNA stage and

by the variable attenuator. The analog section also provides a 1090 MHz signal for test and calibration purpose.

C. Digital section The digital section for the whole system provides a high

sampling rate, up to 100 Msamples/s with a 14 bit resolution. To acquire the linear channel directly at IF the sampling frequency is set at 100 Msamples/s. It is based on the NI PXI 1082 controller. There are three acquisition cards by National Instruments (NI PXI 5122), with two analog input channels each. Figure 3 shows the NI digital section front view: on the left there is the input/output controller, on the right there are the three acquisition devices, each with two analog input and the trigger input.

Figure 4. NI PXI digital section

The digital section share a common clock reference that should be used as phase locked loop (PLL) reference for the analog section. The embedded work suite permits the development of acquisition software.

III. 1090 MHZ CHANNEL ANALISYS

The first TDR implementation is a prototype version composed by a one channel receiver. The receiving chain is splitted before the IF down-conversion to get the LOG channel. Figure 5 shows the front panel of the TDR prototype, figure 6 shows the receiver block scheme.

Figure 5. TDR prototype front panel view

Figure 6. TDR prototype block scheme


Using the TDR prototype, connected to one element of the antenna, it was possible to record the data stream (at 10 Msamples/s) to perform a channel traffic analysis. The results allow traffic statistics related to the area around the installation site. The antenna was positioned in Tor Vergata University on the Engineering Faculty building roof, as shown in figure 7. The location is near Rome, close to Ciampino airport and 30 Km away from the International airport of Fiumicino.

Figure 7. TDR antenna location

The recording session was developed on Thursday 14th April 2011 at 01 p.m.. Up to 10 data streams of 1 s was recorded time continuously. The starting time of each acquisition was chosen random in order to avoid a synchronization with the traffic due to ground radar interrogations. An analysis of the received signals power, considering the receiving chain gain, the antenna gain and the transmitting power, permits to obtain the range distribution of aircraft, which transmitted the received signals. Figure 8 show that a large part of the received signals was transmitted from a range between 40 – 80 km.

Figure 8. Aircraft range distribution

Besides a traffic analysis was done, using a software developed at RadarLab, capable to detect and decode SSR replies using ICAO and RTCA compliance algorithms. The analysis was useful to count the number of conventional and

mode S messages for each data stream, and also to compute the number of overlapping signals.

Table I shows the result of this analysis on the recorded signal segments:

TABLE I. SIGNALS SEGMENTS STATISTICS

Received SSR replies Stream No. # conventional # Mode S # garbled

Mode S

1 859 128 5

2 1155 130 12

3 717 62 6

4 790 99 7

5 1143 92 8

6 990 95 13

7 1236 76 7

8 1695 116 16

9 921 86 4

10 756 57 3

mean 1026 94(8%)

8(8.5%) of Mode S

From table I, the percentage of Mode S signals over all the received signals is 8%. A percentage of 8.5% of the Mode S replies are affected by interference with other signals. To better understand these results, it is possible to note that the probability to receive a 1090 squitter (ES) free of interferences using an omni-directional receiver, is estimated by a poissonian model with (FRUIT rate) equal to the inverse of the average number of received messages per time [6]:

(0) expES ESP t = 0.93

where tES = 120 s, = 1026 s-1. Hence the estimated probability to receive garbled Mode S signals is 7%, the value being close to the experimental rate. An exhaustive measurements campaign, at different time each day, permits to evaluate the channel traffic density near Rome. This earlier result shows that although the FRUIT rate is low, the probability to receive interfered mode S signals is not negligible.

IV. PASA APPLICATION WITH TDR DATA

Using the TDR prototype it is also possible also to test the processing algorithm for overlapping signals discrimination described in [6], where PASA algorithm is proposed for a blind source separation using one channel data. Figure 9 shows a recorded signal with two overlapping Mode S short signals (IF signal by linear channel), with different amplitude: a typical input for PASA algorithm. The signals used for PASA evaluation were sampled at 100 Msamples/s, in order to have more samples to be used for the

Fiumicino airport

TDR antenna

Ciampino airport

5 km 2,7 Nm


de-garbling algorithm. PASA method permits to perform overlapping sources separation exploiting the signals diversity. The array processing methods PA and EPA presented in [2], permits the sources separation exploiting the signals direction of arrival as signals diversity, using an array antenna and a multi-channel receiver. PASA method is based on a signal vector reshaping useful to reorganize the acquired signal samples into a matrix. The idea is to apply PA or EPA onto the reshaped matrix exploiting as signals diversity, not the direction of arrival (that is not recoverable using a single antenna), but the signals frequency. Applying PA or EPA to the data matrix, the mixing matrix and its mixing vectors are estimated. The beamformers of each source is obtained by the pseudo-inverse of the mixing matrix. Applying the data matrix on the beamformers two sub-matrix are computed: one containing the first source, the other containing the second source. To recover the separated signals an inverse re-shaping is applied on the two sub-matrix obtaining the signal vectors.

Figure 9. Two Mode S signals mixing (IF linear channel signal)

Figure 10 shows the results of the application of PASA on the signal shown in figure 9. In this case two overlapping Mode S signals was received, the time delay between the signals is approximately of 46 s, and the power of the second source is less than the first (i.e. it was emitted by a further aircraft than the other). The application of PASA permitted to recover the original two signals, as shown in figure 10 where the envelope of the signals is depicted. The blue line is the first mode S signal, the red one is the second mode S signal. The separated signals are affected by an irregular pulses power behaviors.

Figure 10. Sources signals de-garbled (Envelope signal)

In order to reconstruct the replies, a final stage with a bandpass filter (B=10 MHz) centered at the TDR IF frequency (21.5 MHz), followed by a threshold comparator with 2 levels quantization is used. Figure 11 shows the output of the final stage delivering the reconstructed replies.

Figure 11. Reconstructed replies

These preliminary results are also confirmed by an application of PASA algorithm with another overlapping signal recorded by TDR. In figure 12 it is possible to see the IF sampled signal and in figure 13 and 14 the degarbled and reconstructed signals respectively.


20 40 60 80 100 120-0.01

-0.008

-0.006

-0.004

-0.002

0

0.002

0.004

0.006

0.008

0.01

s

V

Linear channel

Figure 12. Two Mode S signals mixing (IF linear channel signal)

20 40 60 80 100 1200

1

2

3

4

5

6

7x 10

-3

s

V

de-garbled replies

Figure 13. Sources signals de-garbled (Envelope signal)

0 2000 4000 6000 8000 10000 12000 140000

0.2

0.4

0.6

0.8

1

sample

first reply

These results with real data confirms the trials done in [6] and puts the basis for an extensive acquisition campaign useful to measure the effective performance of the algorithm in a real environment.

0 2000 4000 6000 8000 10000 12000 140000

0.2

0.4

0.6

0.8

1

second reply

sample

Figure 14. Reconstructed replies

V. CONCLUSIONS AND FUTURE WORK

The transponder data receiver (TDR) is a multi-channel system useful to receive, record and process 1090 MHz signals from airborne and vehicular transponders (Mode S and conventional). It is composed by a six patch elements array antenna, connected to the 4 linear channel and to a logarithmic receiver. The digital section is based on NI technologies. The TDR system was designed in the research frame on the ADS-B/MLAT to develop and test new signal processing algorithm, and to analyze the 1090 MHz channel traffic. Actually a prototype system based on a single channel receiver is operative at Tor Vergata University. The prototype is composed by a single RF channel divided into a linear channel, directly sampled at the IF of 21.5 MHz, and into a logarithmic channel used as signal detector. The prototype permits to obtain a first channel traffic analysis and the evaluation of PASA algorithm, useful for mixed signal separation using a mono-channel receiver. The first results are encouraging to continue the studies and are helpful for the final TDR version design and implementation.

REFERENCES

[1] M. C. Stevens, “Secondary Surveillance Radar” Artech House 1988 [2] N.Petrochilos, G. Galati, and E. Piracci, “Application of array

processing to receiving stations of multilateration systems based on SSR signals,” IEEE Transactions on Aerospace and Electronic Systems, Vol. 45 n.3 pp. 965-982, July 2009

[3] ICAO Annex 10 to the convention on international civil aviation, Vol. IV, 1998

[4] DO260A, RTCA MOPS for 1090 Extended squitter ADS-B and TIS-B

[5] G. Di Massa, S. Costanzo, A. Borgia, I. Venneri, G. Galati, M. Leonardi, E.G. Piracci, “Multiple sources discriminationa by array processing”,Proceedings of EuCAP’11, Rome 11-15 April 2011

[6] E.G. Piracci, N. Petrochilos, G. Galati, “1090 ES receiving capacity improvement using ADS-B ground receivers with signals discrimination capability”, in proceedings of ESAVS’10, Berlin, Germany 16-17 March 2010


ADS-B/MLAT surveillance system from HighAltitude Platform SystemsMauro Leonardi #1, Silvio Spinelli #2, Gaspare Galati #3

#Tor Vergata UniversityVia del Politecnico 1,00131 Rome, Italy


Abstract—In this work the potential usage of ADS-B and WideArea Multilateration (WAM) Surveillance with High AltitudePlatform Systems (HAPS) is considered. The paper investigatesthe possible configuration of the system, the link budget, the ge-ometry and the limitation due to the random access to the channelby the Mode S Signals (capacity). The surveillance performanceof the proposed architecture in a Wide Area Multilaterationcontext is evaluated by both simulation and statistical analysis(Cramer Rao Lower Bound).

Index Terms—location, air traffic control, multilateration, HAP

I. INTRODUCTION

Automatic Dependent Surveillance -Broadcast (ADS-B) orWide Area Multilateration (WAM) independent surveillanceare becoming of widespread use in modern Air Traffic Man-agement system. These systems use the SSR Mode S channeland the messages emitted from the airplanes to localize andidentify the cooperating targets in their coverage area [1]. Inthe first case (ADS-B) the positions of the airplanes (targets),obtained from the on-board navigation subsystem (usuallyGPS based), are included in the message, in the second onethe target positions are obtained by the system, receivingthe same message at different receiving stations, in differ-ent locations, and then computing an hyperbolic localizationalgorithm. These kind of systems have various advantagescompared with the classical radar surveillance but they havealso some disadvantages related to, for example, the correctpositioning of the various receivers or the coverage of eachstation that could be reduced by blockage from obstacles.Another problem is due to the use a non directional antennain the receiving station. This means that SSR signals fromdifferent directions may overlap in time resulting in reduceddetection and/or decoding performance.

In ESAVS 2010 the DLR-Institute of Space System pro-posed the ADS-B surveillance from satellites and one fea-sibility trial was done using a very low cost stratosphericballoon [2]. That paper clearly shows that there is the basisto a possible deployment of a ”‘flying”’ Mode S surveillancesystems. In this paper we propose an alternative to the satelliteimplementation that is the use of HAPS.

HAPS is proposed because the use of a satellite for lowcost ADS-B payload may be not recommended, due to: (a)unfavorable link budget due to very large distances betweenthe ADS-B receivers and the aircraft, order of one thousand

Figure 1. System architecture. The Mode S signal is received from HAPSand then transmitted to the Central Processing Facility and delivered to theATC Surveillance System.

kilometers; (b) presence of a large numbers of targets in themain beam of the receiving antenna, exacerbating the problemof overlapping replies/squitters.

Besides, HAPS are becoming more and more attractive,as shown by the number of scientific researches about thistype of platform for telecommunication applications [3] [4] [5]and HAPS Mode S surveillance solution has clear advantageswith respect to the ground deployment of the classical system,namely:

• opportunity to ensure the surveillance in regions wherestations can’t be installed (Oceans, deserts, etc..);

• opportunity to ensure the surveillance in the valleys ofmountain regions, where coverage by ground stations isdifficult;

• lower sensitivity of the station to natural disasters (earth-quakes, hurricanes, etc..)

• better robustness with respect to intentional interferencesand Jamming;

and also some advantages with respect to the satellite solution:• possibility to guarantee the surveillance of small region

of the world;• short time to deploy the system;• lower costs.

The disadvantages introduced HAPS Mode S surveillance are:• lower life cycle;• higher cost of implementation.


In Figure 1 the proposed architecture for a generic ADS-B/MLAT stratospheric deployment is shown. This architectureallows, depending on the number of HAPS, the deployment ofmixed systems with different possibilities, i.e.: the possibilityto improve an already existing MLAT/WAM system with oneor more HAPS, the possibility to create an ”‘HAPS only”’WAM system or, finally, to create a simple flying-net of ADS-B receivers. This architecture concerns 4 levels of service: theaircraft is equipped with an ADS-B transponder that computesthe aircraft position using the GNSS system and then transmitsthis information in broadcast; the HAP stations (between 17and 22 km of altitude), receive these information and afteradding the time stamp, the identification number of the stationand the position of the station send the message to the groundCentral processing Station (CPS). In this station the messagecan be decoded and the surveillance of the traffic is performed.If at least four receiving stations (HAP or terrestrial) aredeployed, the Multilateration algorithm can also be performedon Mode S replies and Squitters.

The feasibility aspects, i.e.: weight and dimension of thepayload, geometry (related to signal reception and blockage),link budget and region of coverage of these solution willbe analyzed in the following sections. Finally the channelcapacity, related to the number of fruits in the coverage areawill be considered.

The performance of possible HAPS Mode S systems isreported in the last section with some simulations and trials.

II. GEOMETRY AND COVERAGE FOR SATELLITE ADS-BBefore considering the HAP system a brief analysis of ADS-

B receiver on satellite as secondary application (i.e. ”‘piggy-back”’ payload that must have reduced power consumption,weight and volume to be carried on a satellite designed foranother application) will be done.

Considering the study proposed in [2] the Iridium NEXTSatellites can be used as test bed for satellite ADS-B receiver.This constellation is composed of 66 satellites on 11 orbitalplanes (with an inclination of 86.4°). The satellite altitude is780 km. To assure a global coverage each satellite must havean adequate footprint that call for an antenna beam width 2βm

of about 124°. Considering Figure 2 is possible to calculateall the parameters to describe the coverage of the system, andthe parameter useful to manage the datalink (i.e. the elevationof the satellite and the distance).

Calling the coverage area S, the minimum elevation angleαm, the maximum distance DM and imposing βm equal to62° is possible find all the other parameters (considering alsothe Earth radius RE = 6378 km and Satellite altitude of H =780 km). In particular, defining L = RE +H , the followingequations can be written:

L = DMcos(βm) +REcos(γm) (1)

D2M = L2 +R2

E − 2RELcos(γm) (2)

and is possible to compute γm and αm:

γm = arcos

(L−DMcos(βm)

RE

)(3)

Figure 2. System geometry for a Satellite ADS-B receiver.

αm =π

2− βm − γm (4)

The coverage area S and the coverage radius for each satellitecan be calculated by the following equations:

S = 2πR2E (1− cos(γm)) (5)

D = 2REγm (6)

All the resulting geometry information are summarized inTable I .

H[km] DM [km] αm γm S [km2] D [km]

780 2503.31 7.72° 20.27° 15838703.965 4514.2283

Table ICOVERAGE PARAMETERS FOR AN IRIDIUM SATELLITE WITH AN ANTENNA

BEAMWIDTH OF 124°.

For this coverage it is necessary to verify if the transmittedsignal from the airplanes arrive at the satellite receiver withenough power to be decoded (i.e. greater then the receiversensibility) and also optimize the radiation pattern of theantenna. It is possible to calculate the satellite antenna patternfrom the Friis equation[6]:

Prx = Ptx+Gtx−Ltx−Lat−Afs(βm)+Grx(βm)−Lrx [dB](7)

where Prx is the receiver sensitivity, Ptx is the transmittedpower of the transponder (Class A3 transponder, i.e. 21 dBW),Ltx e Lrx, are the transmitting and receiving losses, Lat is thepropagation loss and Afs is the free-space attenuation.

Ptx Prx Ltx Gtx Lat Lrx

21 dBW -120 dBW 3 dB 0 dBi 3 dB 3 dB

Table IIPARAMETERS FOR LINK-BUDGET COMPUTATION.


Given the parameters in Table II, considering a target atthe maximum distance DM (βm) is possible to compute thefree-space attenuation:

Afs = 10log

(4πDM (βm)

λ

)2

[dB] (8)

and the the Antenna gain directly from the equation 7.In Figure 3 a vertical (azimuth) cut of the pattern is

represented.

Figure 3. Ideal vertical section of the radiation pattern of a full coverageIridium ADS-B antenna.

This kind of radiation pattern cannot be generated with asmall and simple antenna. This means that the ADS-B payloadmust be more complex (multichannel receiver and multibeamantenna), at least as complex as in the primary application ofthe satellite (for example, the antenna should be very near,in dimensions, to the Iridium antenna, i.e. composed by 3panels of about 180 cm X 90 cm). It may be concluded thatglobal coverage by ADS-B on satellites may bring to toolarge and too complicated systems, with an unfavorable costeffectiveness.

III. HAP PAYLOAD DESCRIPTION

Considering the architectures of the system given in Figure1 is possible to define the HAP’s payload for the 1090ESchannel as described in Figure 4. The payload receives theMode S signals and ADS-B report, decodes it and sends theinformation decoded to the CPS. The payload must add infor-mation to every Mode S reply received about Time Of Arrival(TOA) of the message and the platform precise position. So,the platform has a GPS/GNSS receiver. It is important to verifyif this payload respects the requirements to fly on an HAP (interm of weight, volume and power consumption). Therefore abrief analysis of the needed hardware with COTS componentswas done; the results are reported in Table III.

Considering that a typical Unmanned Aircraft System(UAS) or a typical airship for stratospheric fly can carry apayload of 50 kg and can supply a power of kW , the ADS-B/Multilateration receiver, with its weight of less than 5 Kg

Component Weigh Size Power[kg] [cm3] [W]

Antenna 0.1086 8.20× 10× 3 = 246 01090 MHz

Mode S 0.7 3.8× 17.4× 12.4 = 841.46 2.8Receiver

Process and 0.13 9× 9.6× 2 = 172.8 6Control unitGPS antenna 0.111 8.73× 5.59× 3.2 = 156.16 1GPS Receiver 0.54 9.5× 4.2× 16.8 = 670.32 1.7

Estimate Weight, 1.59 2087 11.5size and power

Table IIIPOWER CONSUMPTION, WEIGHT AND VOLUME OF A COTS ADS-B

RECEIVING SYSTEM.

Figure 4. HAP ADS-B Payload diagram

and a power consummation smaller then 3 W can be a sec-ondary payload for an HAP mission (i.e. Telecommunicationor Satellite Navigation augmentation [7]).

IV. COVEREGE OF THE HAP

The real coverage of the HAP system must be also eval-uated. This is essentially limited from: (a) geometry; (b)capacity of the channel and (c) the power, i.e the link budget.

Concerning the geometry and the coverage, it is possible touse the formulas for the satellite application, section II, whileconsidering an altitude of 20 km for the HAP. The results arereported in Table IV.

H DM αm γm βm S D[km] [km] [°] [°] [°] [km2] [km]

20 505.48 0 4.53 85.8203 798038 1008.0220 195.54 5 1.74 83.3 118494 388.43

Table IVPARAMETERS FOR THE MAXIMUM GEOMETRICAL COVERAGE

ACHIEVABLE WITH THE USE OF AN HAP (αm = 0° AND αm = 5°.

Considering that the airplane can be at 10 km of altitudethe real coverage of an HAP can be also larger: for an airplaneflying at 10 km of altitude the maximum distance from theHAP in line of sight is 862.25 km, this means that only oneHAP can cover the European core.

The capacity of the channel depends essentially on thetraffic scenario and on the ability of the receiver to decodesuperimposed replies. Here, the traffic scenarios proposed


in the CASCADE Program from EUROCONTROL [8] isconsidered to exploit the problem of system capacity. TheCASCADE program considers the following scenario: a ModeS receiver in Bruxelles at an altitude of 3300 feet and acoverage area with a radius of Rmax = 300NM with threescenarios for the fruit densities:

1) high interference (λmax,1 = 105000 fruits per second)2) reduced Radar infrastructures (λmax,2 = 55000 fruits

per second)3) medium interference (λmax,3 = 50000 fruits per second)4) low interference (λmax,4 = 27500 fruits per second).

If an uniformly distributed traffic in the coverage area and anarrival process of the fruits Poisson distributed are considered[9], it is possible to write:

p(n) =(λT )n

n!e−λT (9)

in which p(n) is the probability of receiving n replies in thetime interval T (length of the Mode S reply), for a fixednumber of fruits per second λ. When n is larger than oneit means that one or more fruits arrive before the end of thereply, i. e. interference condition. Not all the tranponders inthe coverage area will interfere with a reply but only the replythat have enough power to produce a Signal to InterferenceRatio (SIR) smaller then a given value. For this reason only themessage coming from airplanes which have a distance fromthe receiver between 0 and R+Δr are considered, where R isthe distance of the interfered airplane and the receiver and Δris such that the received power of the interfering reply is noless than 3dB below the received power of the interfered reply.This means that it is assumed that a signal to interference ratiogreater than 3 dB can be managed. In Figure 5 the probabilityof receiving a non interfered signal from a transponder at agiven distance R using a standard receiver is reported; we haveassumed the previous hypothesis and that the number of fruits(λ) is computed by using the effective radius of the interferingarea:

λi =π(R+Δr)2

π(Rmax)2· λmax,i (10)

It is possible also to consider an enhanced receiver thathas the capability to decode the message also in the caseof interference conditions, as described in [9]. In particular,calling tp the Mode S preamble duration and tES the ModeS reply duration the following events can be considered:

• A:(0 interfering signals in [0− tES ]);• B:(0 interfering signals in [tp − tES ]);• C:(1 interfering signals in [tp − tES ]);• F:(0 interfering signals in [0− tp]);

and for this enhanced receiver we can say that we do not haveinterference if:

Pfree = P (B ∪ C|F ) =P ((B ∪ C)F )

P (F )=

P (BF ) + P (CF )

P (F )

=P (A) + P (C)P (F )

P (F )=

P (A)

P (F )+ P (C)

(11)

Table VCOVERAGE LIMIT DUE TO THE DIFFERENT RECEIVER CAPABILITY

(STANDARD (*), ENHANCED RECEIVER FOR ONE (’), TWO (”), THREE (ˆ),INTERFERING SIGNALS).

this means that if only the preamble is free of interferenceand the data-block has zero or one superimposed signals, themessage can be considered free of interference because theinterference can be managed and solved [9]. In the samemanner also the interference with 2 or 3 replies can bemodeled. Table V shows the resulting coverage area (due tothe capacity of the channel) for different scenarios.

Figure 5. Probability of receiving one long squitter without interference.

In the Table V it is possible to see that the capacity ofthe channel (i.e. the number of aircraft in the coverage of thesensor) is the most important factor that limits the system’sperformance, only using enhanced receiver is possible to


achieve a coverage area range of 136 NM for a single reply(PD = 0.9) or 232 NM for 9 replies (at least 1 reply every 10seconds).

The last system parameter to be investigated is the linkbudget. Staring from the Friis equation, it is possible to changethe coverage area choosing the appropriate antenna for thereceiving station.

In figure 6 the ideal vertical section of the radiation patternof an HAP ADS-B antenna (from 20 Km of altitude) is shown,it is computed with the same formulas used for computing thesatellite ideal radiation pattern.

Different types of antenna have been analyzed from a 20Km high HAPS, in particular:

• Dipole antenna;• Vertical array of 4 dipoles with about 8 dBi of maximum

gain that allows 60 NM of coverage;• Vertical array of 6 dipoles with about 9 dBi of maximum

gain that allows about 120 NM of coverage.

Figure 6. Ideal vertical section of the radiation pattern of a ideal HAP ADS-Bantenna.

In table VI the coverage due to the antenna configurationare reported and the considered capacity scenario are alsocompared with the antenna coverage. It is possible to see thatexisting configurations for HAP application in high traffic orlow traffic condition match also with the proposed antennas.

V. INNSBRUCK WAM - SIMULATION TRIALS

To understand the capability of an HAP ADS-B receiversome simulations were done. In particular the real InnsbruckWAM system was supposed to be enhanced with an HAPplatform. The actual Innsbruck WAM system consists of 9stations (Figure 7): 3 transmitting and receiving station, 5receiving -only, and one reference transponder (Patscherkofelstation) [10]. Considering the position of all the sensors (TableVII) we add an HAP (204 km of altitude) at coordinates:(47°16’N, 11°23’E).

It is possible to understand the performance of these systems(with or without the HAP receiver) using the Cramer Rao

Table VIPROPOSED ANTENNA FOR THE CONSIDERED TRAFFIC SCENARIOS.

A:{DIPOLE, AVIONIC ANTENNA}, B:{4 OR 6 ARRAY OF DIPOLES)}.

Position Latitude Longitude Altitude [m]Patscherkofel 47°12′31.4′′ 11°27′36.7′′ 2245

Flughafen 47°15′28.2′′ 11°21′9.8′′ 616Hafelekar 47°18′46.4′′ 11°23′10.3′′ 2336

Rangger Kopfl 47°14′37.4′′ 11°10′51.9′′ 1910Tulferer Berg 47°15′11.6′′ 11°33′20.9′′ 1360

Hecher 47°19′15.7′′ 11°44′30.8′′ 1895Kanzelkehre 47°24′34.6′′ 11°47′15.1′′ 1006

Telfs 47°18′11.9′′ 11°04′23.1′′ 605Gschwandtkopf 47°18′49.3′′ 11°10′39.5′′ 1459

Table VIIWAM INNSBRUCK- STATIONS COORDINATES [10].

Figure 7. Innsbruck WAM real layout and taking off path.


Figure 8. Comparison of CRLB for a taking off airplane in the Innsbruckscenario with or without one HAP over the airport.

Lower Bound (CRLB) method described in [11], consideringa TOA accuracy of:

σTDOA,i(θ) = max

{1

B√2SNR

, 1

}[meters] (12)

Where B is the receiver bandwidth supposed equal to 20MHzand SNR the signal-to-noise ratio.

The CRLB was computed along the taking off path reportedin Figure 7 and the lower bound for Vertical (

√σ2z ) and 3D

error (√σ2x + σ2

y + σ2z ) are reported in Figure 8.

Figure 8 clearly shows the benefits introduced by usingthe HAP over the airport. The height of the HAP allowsimprovement of the Dilution Of Precision (DOP) and thereforeof the system accuracy (e.g. 3D position error below 5 metersuntil 40 Km of coverage). This kind improvement cannot beobtained with any type of ground stations.

VI. CONCLUSION

The paper shows that it is possible to develop a low costADS-B Mode S receiver to be installed in a HAP with a very

simple antenna and receiver. The problem of the coverage ofthis kind of station was discussed and different solution havebeen proposed for high density and medium density traffic. Acoverage radius greater than 140 NM (limited by the capacityof the channel) can be achieved with all the advantages due tothe fact that we have a flying receiver station. The advantagesof having a flying station is very clear when the station is a partof Multilateration system (Local or Wide area), in this casethe performance increases a lot with respect to the classicaldeployment due to the high decrease in the value of the verticalDOP, as shown with simulation for the Innsbruck WAMsystem. The aim of the paper was to study the possibilityto develop a small, not heavy and cheap system to be usedas a piggy-back system over the primary application of theHAP (telecommunication or satellite navigation applications)but the results call for a study for the development of a morecomplex system with a multibeam antenna that can managemore than one independent coverage area (this is possiblebecause the maximum geometrical coverage is about 500 NM)with an ad hoc designed platform with a big array antenna anda multichannel receiver.

REFERENCES

[1] M. Leonardi, G. Galati, P. Magaro, and V. Paciucci, “Wide area surveil-lance using ssr mode s multilateration: advantages and limitations,” inEuropean Radar Conference, 2005, Parigi, 6-7 october 2005, p. 225.

[2] T. Delovski, L.-C. Hauer, and J. Behrens, “Ads-b high altitude measure-ments in non radar airspace.” ESAVS 2010 proceedings, 16-17 march,2010, berlin, pp. 1-5.

[3] A. Aragon-Zavala, J. L. Cuevas-Ruız, and J. A. Delgado-Penın, High-Altitude Platforms for Wireless Communications. John Wiley and Sons,2008, pp. 1, 16-33, 155-157.

[4] R. Miura and M. Suzuki, Preliminary Flight Test Program on Telecomand Broadcasting Using High Altitude Platform Stations. WirelessPersonal Communication 24, 2003, pp.341-361.

[5] D. Grace, K. Katzis, D. Pearce, and P. Mitchell, “Low-latency mac-layerhandoff for a high-altitude platform delivering broadband communica-tions.” The Radio Science Bulletin No 332, March 2010.

[6] H.T.Fris, “A note on a simbol trasmission formula,” May 1946, vol.34,pp.254-256.

[7] F. Dovis, L. L. Presti, and P. Mulassano, “Support infrastructures basedon high altitude platforms for navigation satellite systems,” WirelessCommunications, IEEE, vol. 12, no. 5, p. 106, october 2005.

[8] 1090 MHz Capacity Study-Final Report, Cascade program Std., July2006, edition 2.6, pp.13-25, 32-34, 46-54.

[9] E.G.Piracci, N.Petrochilos, and G.Galati, “1090 es receiving capacityimprovement using ads-b ground receivers with signals discriminationcapability.” ESAV’08 Proceedings, 3-5 September 2008, pp. 1–7.

[10] Wieser, Wolfmayr, Langhans, Cernin, and Scheiflinger, “Wide areamultilateration at terminal area innsbruck,” in Wide Area MultilaterationWorkshop, Eurocontrol, Bruxelles, June 2007.

[11] G. Galati, M. Leonardi, and M. Tosti, “Multilateration (local andwide area) as a distributed sensor system: Lower bounds of accuracy.”Amsterdam: EuRad 2008 Conference, 30-31 October 2008.


Space-based ADS-B A small step for technology a giant leap for ATM?

Adam Parkinson Senior Consultant

Helios Farnborough, UK

[email protected]

Abstract - This paper investigates the feasibility and merits of a space-based ADS-B system. The primary concept that will be investigated is the reception of ADS-B transmissions from aircraft in oceanic airspace using satellites and the subsequent relay of the data to end-users on the ground.

Keywords: ADS-B, space systems, new concepts, applications, oceanic, requirements)

I. THE CHALLENGES FACING AIR TRAFFIC MANAGEMENT

There are several significant challenges facing Air Traffic Management (ATM) today. These can be summarized as the need for increased capacity to cope with traffic growth whilst at the same time reducing delays. Across Europe these challenges are being strategically addressed through two initiatives. The Single European Sky (SES) is concerned with providing the necessary legislative framework while the SES ATM Research (SESAR) Programme is concerned with the modernization and interoperability of ATM infrastructure across Europe. In particular SESAR has the following performance targets:

• Accommodate a 3 times increase in movements whilst reducing delay.

• Improve safety by a factor of 10.

• Enable a 10% reduction in environmental effects per flight.

• Reduce ATM unit cost to airspace users by at least 50%.

II. THE ROLE OF ADS-B For SESAR and NextGen (the US equivalent), Automatic

Dependent Surveillance Broadcast (ADS-B) is one of the most important underlying technologies in the plan to transform ATM from the current radar-based surveillance to Global Navigation Satellite Systems (GNSS) surveillance. ADS-B is defined by the International Civil Aviation Organization (ICAO) as a surveillance application transmitting parameters, such as position, track and ground speed, via a broadcast mode data link, and at specified intervals, for utilization by any air and/or ground users requiring it. The ADS-B reports are sent periodically by the aircraft with no intervention from the ground function and ADS-B reports may be received by any suitable receiving equipment in range of the transmitting

aircraft. The data transmitted is derived from the aircraft systems themselves and in this sense ADS-B is known as a dependent surveillance technology. The transmitting aircraft does not know which, if any, recipients are receiving and processing the position reports as they are not acknowledged. The concept with ADS-B is that position reports are transmitted so frequently that the loss of a small number of position reports is not operationally significant.

ADS-B does not require a specific data link however throughout the rest of this paper when referring to ADS-B we mean the transmission of Extended Squitter messages transmitted over the 1090MHz channel 1090ES. The 1090MHz channel is the downlink channel for Secondary Surveillance Radar (SSR) replies from aircraft and the Extended Squitter messages are compatible with the Mode S radar data link formats. Amongst other data items Extended Squitter messages contain aircraft position information derived from GNSS.

The potential benefits of ADS-B are [1]:

• Faster data update rate than that typically available with radar.

• Lower cost ground infrastructure compared to radar.

• Surveillance data can be directly received in the aircraft cockpit increasing situational awareness.

• Position accuracy is potentially higher than radar and is not range dependent.

• It can display both airborne and ground traffic.

• It can potentially enable new applications and operational procedures resulting in more efficient flight profiles and reduced emissions e.g. allows 5 NM of separation in Non-Radar Airspace (NRA) compared to current procedural separation.

However, in order to realize the full benefits from ADS-B aircraft must be equipped and plans for mandates in both Europe and the US are illustrating the difficulty in achieving universal equipage and the need to ensure that once fitted the avionics are fully utilized. The mandates are currently being put in place in the US and Europe for 1090ES ADS-B (dates 2015-2020) and 1090ES ADS-B is already widely implemented on commercial traffic. In addition ADS-B is being deployed world-wide in places like Australia, Canada, Thailand, Jamaica, United Arab Emirates and South Korea.


Other potential issues related to ADS-B include [2]:

• Incorrectly coded 24-bit (technical) aircraft address used to uniquely identify aircraft.

• Dependent on GNSS for surveillance and navigation, and GNSS position potentially leads to oscillating position quality.

• May need addition surveillance cover provide by independent surveillance technology to provide a separation service in current radar airspace.

III. SURVEILLANCE OF REMOTER REGIONS

A. The oceancic problem Traditionally surveillance has been performed by

monitoring on the ground signals received from aircraft, requiring ground infrastructure to do so. However, in oceanic or remote regions it may not be practically possible to install ground infrastructure even with ADS-B.

For long periods of time in oceanic airspace, aircraft are unable to communicate directly with controllers and are therefore issued with strategic clearances between exit and entry points to what is know as procedural airspace. Because of the lack of communication and surveillance information, aircraft in oceanic airspace are required to maintain large separation distances/times, typically 10 minutes longitudinally and 60NM laterally, in order to maintain safety standards [3]. This procedural separation has the undesired effect of limiting airspace capacity and also the flexibility of aircraft to fly efficient routes.

The question therefore arises how can we make use of the ADS-B surveillance data already being transmitted by aircraft to improve oceanic operations?

B. Current oceanic operations and technical solutions Aircraft with different speeds on the same track in oceanic

airspace will gradually get closer or further apart. It is imperative to monitor this change of spacing closely for loss of separation. Pilots are therefore required to report their position verbally at regular intervals along the route, for example at each waypoint or every 45 minutes which ever is shorter [3]. Typically this is using High Frequency (HF) or satellite communications.

However, the advent of data link communications has already removed the need for voice reporting and enabled higher rates of position reporting in oceanic regions for suitably equipped aircraft. This type of position reporting is known as Automatic Dependent Surveillance Contract (ADS-C). It is based on setting up a point-to-point communications contract between the aircraft and the ground where position reports are acknowledged and are either made at an agreed regular rate, are event driven or are made on demand.

The only operational implementation of ADS-C in oceanic airspace is the Future Air Navigation System (FANS) 1/A equipment on Boeing and Airbus aircraft. Nearly all long haul aircraft are now equipped with FANS 1/A. FANS 1/A makes

use of the Aircraft Communication Addressing and Reporting System (ACARS) and sub-networks used by many aircraft to communicate aircraft information to the Airline Operations Centre (AOC).

The increased monitoring provided by FANS 1/A ADS-C services potentially enable the following benefits in oceanic airspace [4]:

• reduced separation (typically 30NM longitudinally and laterally);

• more direct routes;

• more optimal climb and descend profiles;

• increased access to cruise altitudes or closer to optimal;

• reduced controller and pilot workload;

• increased level of safety.

However, it is unlikely that ADS-C data will ever be used operationally to provide a radar-like separation service in oceanic airspace. The application of ADS-C based separations would require extensive evaluations and agreements with adjacent Area Control Centers (ACC) [4].

C. Future oceanic operations and technical solutions A potential future ADS-B enabled application for oceanic

airspace is the In-Trail Procedure (ITP). It was originally envisaged that this could be provided by ADS-C. However, it was decided that this was an impractical solution, and that any airborne surveillance application used in the oceanic airspace should be feasible without ground surveillance [5]. Therefore, the application is now either Airborne Traffic Situational Awareness (ATSA) with similar procedural limits to today or Airborne Separation (ASEP) with new airborne separation standards (yet to be defined).

ATSA-ITP has some of the most noticeable benefits for a relatively small investment and is therefore likely to be one of the first airborne applications to be implemented. Although originally a spacing application, ATSA-ITP has now been re-classified as situational awareness. The ATSA-ITP allows pilots to identify the relative position of other aircraft, and pass this information to the controller to clear the aircraft for a procedural climb. Due to the higher accuracy of surveillance information available to the controller (and flight crew) via the ADS-B reports, lower procedural limits can be applied during the duration of the climb, assuming some geometric constraints. Since the separation limits are lower, there is effectively more airspace within which the aircraft can climb, thus maximizing the fuel efficiency for the given traffic density [5].

ATSA or ASEP applications may also enable additional procedures which are not possible with just ADS-C, such as passing maneuvers, to be implemented in procedural oceanic airspace.


IV. SATELLITE ADS-B

A. Why do satelite ADS-B? One of the aims of SESAR is to develop a global

interoperable ATM system. ADS-B is a significant contributor to a future ATM system capable of providing high accuracy, high update rate position reports with a low cost ground infrastructure. Satellites also play a key role in enabling global communications services and high accuracy positioning and navigation through GNSS. Given that aircraft will be equipped for ADS-B operation over continental regions it is only natural to investigate the feasibility of receiving the ADS-B position reports via satellite to provide cost-effective surveillance coverage in remote regions without current surveillance infrastructure. The potential benefits are the improved monitoring of aircraft in remote areas to increase safety and enabling subsequent changes to current procedures to make more efficient use of the airspace.

B. Assessment of a potential implementation A key question to answer is whether satellite ADS-B is

feasible, or under what conditions is it feasible? To help us answer this question we postulate a possible satellite ADS-B implementation and assess its potential performance characteristics.

The analysis consists of an ADS-B receiver installed on a satellite in a sun synchronous orbit at an altitude of 670km receiving ADS-B reports at 10 locations in the North Atlantic oceanic region. The assumptions used in the analysis are summarized in Table I. A visual representation of the scenario is presented in Fig. 1 where the red crosses represent the 10 analysis points, the blue triangles are aircraft and the purple circles are radar locations [6].

C. Assessment results and analysis The 1090MHz interference received by the satellite ADS-B

receiver at each of the test points was analyzed using a 1090MHz interference model developed by Helios on behalf of Eurocontrol. From the analysis the following observations were noted:

• The highest interference levels are recorded at test points that either have the highest traffic densities or are within range of radar interrogations.

• The level of TCAS interference is generally low as the test point locations are primarily in areas of low or medium traffic density and the probability of an aircraft being close enough to generate TCAS transmissions is low.

• The level of Mode A/C SSR interference is low as most test points are out of range of SSR radars.

TABLE I. ANALYSIS SCENARIO ASSUMPTIONS

Scenario element Description

Aircraft

Predicted 2015 oceanic and core Europe traffic levels based on actual Eurocontrol CFMU data and STATFOR growth predocitions Mode S equipage 100% Extended Squitter equipage

100% of Mode S equipped aircraft

TCAS equipage 80%

Aircraft Mode S transponder

Short Squitter trasnmission rte 1Hz

Extended Squitter transmissions rate 6.2Hz

Transmission power 57dBm

Antenna pattern Omni-directional with 0dB gain

Radar

Predicted Mode S and SSR radar installations in Europe in 2015 based on information gathered from Eurocotnrol and European ANSPs Number of civil SSR 20 Number of military SSR 340

Number of civil Mode S 160

Number of military Mode S 45

Satellite

ADS-B receiver installed on a satellite in a sun synchronous orbit at an altitude of 670km Antenna horn size 5mm

Antenna peak gain 9.6dB

Antenna beamwidth -3dB

Antenna pattern shape

Elliptical with semi-major aperture of 25 degrees and semi-minor aperture of 22.5 degrees

Cable losses 0dB Minimum signal level for detection -92dBm

Figure 1. Satellite ADS-B analysis scenario


The figures below show the detailed analysis of a high interference test point in the mid-Atlantic which contains 32 aircraft within the satellite spot beam. Fig. 2 shows the frequency and type of messages received on 1090MHz split into four 90 degree sectors while Fig. 3 shows the cumulative total of messages against the received power [6]. Fig. 4 presents results from a similar analysis to predict interference levels on the ground and in the air in mainland Europe [7].

The interference at the satellite ADS-B receiver is much lower than that expected at an ADS-B ground station in mainland Europe. An ADS-B ground station needs to be able to decode wanted Extended Squitters from aircraft in its operational range in the presence of unwanted transmissions or transmissions from aircraft outside of its operational range. However, the majority of signals received at the satellite will be Extended Squitters which we want to decode. The Extended Squitters also come from aircraft which are at similar distances from the satellite and are therefore likely to have similar received signal powers. Therefore it is highly likely that any overlap between the received transmissions will lead to garbling of the Extended Squitter.

Using the assumption that any overlap between received messages would lead to garbling, and an interference model developed as part of the Eurocontrol ADS-B Coverage Analysis and Planning Tool (CAPT), we further analyzed the potential Update Probability (UP) for receiving ADS-B position updates [8]. Assuming a 2Hz transmission rate for Extended Squitters containing position data the satellite ADS-B receiver could support up to 160 aircraft in the spot beam in an oceanic region whilst still achieving a 95% probability of update within 5 seconds. However, it should be noted that this figure is likely to decrease dramatically as aircraft within the spot beam fall within radar cover and interference levels increase.

Mode S inteference in each of four 90 degree sectors

0

20

40

60

80

100

120

1 2 3 4Subsector

FRU

IT (H

z)

Mode S TCAS replies

extended squitters

Short squitters

All-call ModeS (own)

All-call ModeS (other)

Roll-call ModeS (other)

Figure 2. Detailed analusis of interference in mid-Atlantic

Cumulative 1090MHz interference

0

50

100

150

200

250

300

-97 -92 -87 -82 -77

Received Signal Level (dBm)

Num

ber o

f Mes

sage

s pe

r sec

ond

Mode A/CShort Mode SExtended SquitterTotal Mode S

Figure 3. Cumulative interference against receieved signal level

Results from study predicting interference levels at Brussels in 2015

0

20000

40000

60000

80000

100000

120000

Ground test point (0km) Airborne test point (10km)

FRU

IT (H

z)

Mode A/C TCAS

Mode A/C

Short Squitters

Mode S All Call

Mode S TCAS

Extended Squitter

Roll Call Mode S

Figure 4. Predicted levels of 1090MHz interference in mainland Europe

Considering the onward transmission of the ADS-B data received by the satellite there are two methods:

• Bent-pipe where the data received by the satellite is forwarded on to the ground with amplification and a shift to the downlink channel frequency.

• Regenerative where the data is decoded by the satellite and then and re-encoded onto the downlink signal.

Table II provides a comparison of these two methods for the onward transmission of the received ADS-B data against the likely ADS-B data rate requirements. Considering this analysis and the analysis above the initial conclusion is that from a technical point of view it is feasible to receive ADS-B data using a satellite.


TABLE II. COMPARISON OF TWO METHODS FOR ONWARD TRANSMISSION OF ADS-B DATA

1090Mhz ES Bent-pipe Regenerative

Channel bandwidth

2.6Mhz (-3dB point)

Typcially 10’s of MHz for Ka/Ku band [9]

Typcially 10’s of MHz for Ka/Ku band [9]

Data rate

22/111 Kpbs required for decode of Extended Squitters from 32/160 aircraft

Typically 1000’s Kbps per sub-channel [9]

Typically 1000’s Kbps per sub-channel [9]

Latency ~0.5 seconds Up to 2 seconds [10]

Pros

Shorter latencies

Offers ability ro post process signal on ground for better decode performance

More efficient use of downlink bandwidth

Time of applicability of ADS-B report can be adjusted for latency of satellite link

Cons

Time of applicability of ADS-B report ignores latency of satellite link

Less efficient use of downlink bandwidth

Longer latencies

D. Other satellite ADS-B initiatives The feasibility and potential benefits of tracking aircraft

(and ships) via satellites are now being more and more widely recognized. There are several other recent initiatives investigating the feasibility of providing satellite tracking services and these are summarized below identifying any further lessons that can be learned.

The SESAR project OPTIMI is currently investigating technical solutions including the potential for satellite services to improve monitoring and position tracking of aircraft while in remote or oceanic areas. In particular this is to support Search and Rescue operations and accident investigation in these remote areas. The project aims to deliver recommendations that can be implemented in 2011. ESA has also recently launched an invitation to tender to procure an ADS-B payload for the reception and processing of ADS-B signals on a satellite in-orbit demonstration mission.

This year the satellite service provider Globalstar has signed an agreement with ADS-B Technologies in order to develop a system allowing ADS-B equipped aircraft in remote and oceanic regions to relay information to the ground and other aircraft using the Globalstar network of satellites and ground stations. The proposed Globalstar service is based on a constellation of low-earth-orbit satellites using a bent-pipe architecture which they claim will provide near-real-time data relay.

The Satellite service provider Iridium also announced this year that they plan on monitoring ADS-B transmissions using their next generation of 66 communication satellites. Their current generation of satellites is currently being certified to provide aviation safety services requiring the satellites meet defined levels of robustness, reliability and latency. The next generation of satellites will be fully operational by 2017 providing global coverage. The main technical challenge being investigated is the need to blank out ADS-B reports received from high density areas as these could saturate the ADS-B receiver.

Similar initiatives exist, and in many cases are further advanced, in the maritime domain for tracking ships using satellites and AIS - the maritime equivalent of ADS-B. Originally designed as a terrestrial system, there are now AIS receivers installed on satellites decoding AIS transmissions from ships. The Canadian company ExactEarth is already offering a commercial space-based AIS service using two microsatellites. It also has plans to launch two more microsatellites this year that will enable post-processing of the received AIS data on the ground to improve probability of detection performance. However, the requirements for the use of the AIS data may be different from the potential use of ADS-B data. The dynamics of a ship are very different to aircraft and AIS data is not used for providing separation services or collision avoidance. Satellite AIS data is often fused with other remote imaging data and the primary applications under investigation are homeland security, search and rescue and environmental monitoring. Current performance targets are of the order 80% probability detection and update rates in the order of minutes or even hours rather than seconds. Experience from current test or operational AIS satellites also indicate that probability of detection performance can be variable [11].

V. CONCLUSIONS

The assessment of a potential implementation of satellite ADS-B demonstrates the feasibility of decoding ADS-B messages with a high update probability via an ADS-B receiver installed on a satellite. The assessment also provides evidence that satellite ADS-B can successfully provide aircraft position updates to controllers at update rates similar to radar, and much higher than ADS-C, even considering oceanic traffic growth well beyond 2015. However, it is noted that this conclusion only holds true when the aircraft are out of the range of SSR radars. When aircraft are within radar coverage (e.g. close to shore) the interference environment experienced by the satellite will increase dramatically and detection performance will drop.

It is unlikely satellite ADS-B will ever enable surveillance separations in oceanic airspace that are currently achievable in Radar (RAD) airspace because for safety reasons a second layer of surveillance cover would be required that is sufficiently different from ADS-B. However, satellite ADS-B, particular when combined with ADS-B ATSA or ASEP applications may:

• Provide a cost-effective means of monitoring from the ground ATSA and ASEP enabled maneuvers in oceanic airspace (such as passing) providing increased safety.


• Enable more efficient flight procedures such as those enabled by ADS-B ground stations installed in Non-Radar Airspace (NRA).

The ADS-B-NRA application is designed to enhance the following ICAO air traffic services:

1) ATC service and flight information service principally for:

• ATC separation services, including the possible reduction of separation minima;

• transfer of responsibility for control;

• ATC clearances;

• flight information services;

• flight crew guidance for flight operations in ADS-B only surveillance airspace.

2) Alerting services, principally for: • notification of rescue coordination centers;

• plotting of aircraft in a state of emergency or when deviating from intended track (e.g. because of bad weather).

However, the following critical differences between the application of satellite ADS-B in the oceanic region and ADS-B-NRA should be noted:

• the increased latency of ADS-B reports received by a satellite compared to a ground station;

• the lack of real-time voice (or data) communications in oceanic airspace compared to NRA.

In terms of implementation timescales it is likely that initial ADS-B satellite services may be available around the time of the European ADS-B mandate in 2015. The maritime domain is leading the way in that it already has commercially available satellite tracking services and satellite service providers have identified that a similar business opportunity may exist in aviation.

However, a word of caution: initial maritime applications appear to be focused on homeland security and environmental monitoring applications. If satellite ADS-B is to be used to improve the efficiency of oceanic operations through new procedures and reduced separations its actual performance in terms of probability detection, update rates, latency etc. must be demonstrated through live trials. Decisions on optimum spot sizes and the total number of satellites required to give sufficient satellite coverage also need to be made. Furthermore, if the satellite ADS-B concept is to be developed within the aviation community a rigorous cost-benefit analysis is required to ensure that additional benefits enabled by satellite ADS-B are cost-effective compared to other technical solutions.

REFERENCES

[1] W. Richards, K. O’Brien, D. Miller, New air traffic surveillance technology, Boeing aero quarterly, quarter 2 2010, unpublished.

[2] B. Stanley, S. Kelly, Helios new surveillance technologies, Helios 2009, unpublished.

[3] Joint North Atlantic airspace standard operating procedures, August 25 2006, unpublished.

[4] Guidance material on SISTAL FANS-1/A implementation on SAL airspace, unpublished

[5] B. Stanley, S. Kelly, Helios ADS-B training course, 2008, unpublished. [6] A. Parkinson, P1175 ADS-B satellite 1090MHz scenario simulation

technical note, version 0.2, September 2009, unpublished. [7] R. McDonald, CASCADE programme 1090MHz interference study –

final report, version 2.3, July 2006, unpublished [8] A. Parkinson, P895 CAPTv2 software requirements specification

(algorithms), version 0.k, December 2010, unpublished. [9] M. Bever, J. Freitag, S. Linsky, J. Myers, R. Nuber, J. Prieto Jr., E.

Wiswell, Fast-packet vs. circuit switch and bent pipe satellite network architectures, Fourth Ka-Band Utilization Conference Venice, Italy, November 1998

[10] O. Gupta, Global augmentation of ADS-B using Iridium NEXT hosted payloads, February 2011, unpublished.

[11] F. te Hennepe, R Rinaldo, A. Ginesi, C. Tobehn, M. Wieser, Ø. Helleren, Feasibility of an European constellation for space-based detection of AIS signals, European symposium on satellite-AIS, December 2010.


1

Strategies to Design and Deploy Mode S Multilateration Systems


Correction of systematic errors in Wide Area Multilateration


Multilateration system time synchronization via over determinationof TDOA measurements


Improvement of Multilateration (MLAT) Accuracy and Convergencefor Airport Surveillance


Assessing the safety of WAM over a non radar surveillance area



Strategies to Design and Deploy Mode-SMultilateration Systems

Ivan A. Mantilla-G, Juan V. Balbastre-T,Elías de los Reyes

Instituto ITACAUniversidad Politécnica de Valencia

Valencia, Spain{imantilla, jbalbast}@itaca.upv.es, [email protected]

Mauro Leonardi, Gaspare GalatiRadar and Navigation Laboratory

Tor Vergata UniversityRome, Italy

{leonardi, galati}@disp.uniroma2.it

Abstract In this paper, we study and develop some strategies to design and deploy Mode-S multilateration systems. These strategies are based on metaheuristic optimization techniques, like Genetic Algorithm (GA) and are intended to obtain useful parameters for an optimal system configuration that provides acceptable performance levels. Furthermore, these strategies are able to evaluate and improve previous system designs. Parameters such as the number of stations, the system geometry, the kind of measurements to be used and system accuracy are obtained taking into account requirements such as the line of sight, the probability of detection and the accuracy levels.

Keywords- multilateration; air traffic control; optimization;metaheuristic methods.

I. INTRODUCTION

Multilateration Systems (MLAT Systems) are a powerful option for the surveillance function of air traffic control. These systems are intended to inform air traffic controllers of the location and identification of aircraft (taxiing, taking off / landing, approach or enroute) or vehicles equipped with an operational SSR transponder [1]. To perform these functions, a number of ground stations (at least three for 2D or four for 3D), with capabilities to measure some characteristics of the Mode-S signals, emitted by the transponders (e.g. Time of Arrival -TOA-, Round Trip Delay -RTD- or Angle of Arrival AOA-), are placed in some strategic locations around the airport or the area to be covered and connected with a Central Processing Subsystem (CPS).

The accuracy of position estimation in MLAT systems basically depends on the stations positions [2-5]. To design and deploy these systems, one should consider multiple factors such as the Line of Sight (LoS) of each station, the probability of detection, the accuracy, the redundancy, etc., and they deploy all the stations, to obtain the maximum possible system coverage, respecting all the regulatory standards (e.g. those described in [1]) and the many constraints imposed by the particular site. In many cases, choosing the number of stations and their locations to meet all the requirements is not an obvious task and the system designer has to do several designs, by trial and error, before obtaining a satisfactory spatial distribution of the stations.

A first application of the metaheuristic optimization techniques, to design multilateration systems, was presented in ESAVS 2010. That work [6] proposes the use of Genetic Algorithms to obtain an optimal distribution (system geometry) of a given number of MLAT ground stations only taking into account the line of sight and the Dilution Of Precision (DOP). In [6] only Time Difference of Arrival (TDOA) measurements have been considered. However, there are other relevant parameters that should be taken into account in order to obtain a more realistic design. Another important aspect is that the DOP only reflects the errors due to the spatial distribution of the stations, regardless of other important sources of errors (e.g. errors due to propagation effects, which are site-dependent, instrumental errors due to time stamp, etc.).

This paper presents an evolution of the previous work [6] with the introduction of more relevant parameters and a more rigorous formulation to evaluate the system accuracy (the Cramér-Rao Lower Bound -CRLB- analysis described in [2]). The possible implementation of the system with other kind of measurements, like RTD or AOA, is also evaluated. Moreover, the strategies developed herein are able to evaluate, validate and improve previous systems designs.

II. GENERAL PROCEDURE DESCRIPTION

The strategies developed in this work are based on the design of a new standard MLAT system (e.g., with only Time Difference of Arrival -TDOA- measurements) or of its improved version (e.g., with the combination of TDOA/RTD or TDOA/AOA). In this work, the system design is obtained by calculating the minimum number of stations and their locations (sites coordinates), that maximize the line of sight coverage and system accuracy. These calculations are performed under some regulatory constraints [1] or by those that are intrinsic to the airport layout, e.g. there are forbidden areas (clearances) or the available sites are restricted to some specific areas. In all cases these constraints can be modified to satisfy some particularities of the design.

The procedure proposed here is also useful to analyze if any previous design is the optimum solution for a given resources or whether it could be improved by some feasible but not obvious position changes of the stations.

Mr. Ivan A. Mantilla-G has been supported by a FPU scholarship (AP2008-03300) from the Spanish Ministry of Education.


Figure 1. General design procedure.

Unlike the previous work described in [6], where the search space (the set of available station sites) is composed by the entire airport area (i.e., a relative continuous space), in this work, due to real constraints like power supply, sites availability, etc., we have limited that search space only to a set of P sites. The latter allows obtaining more realistic designs. The complexity of this problem, for a number of stations (with <P) can be evaluated by,

Equation (1) provides the number of possible combinations given the size of the discrete search space P and the number of stations to be deployed . The procedure used in this work is based on that one proposed in [6] but, here several aspects for each step have been modified and added. The updated procedure is shown in Fig. 1. This procedure is composed of three steps, namely, Initialization, System Design Evaluation and Genetic Algorithms. In the following the updated and new aspects are described.

In the first step (Initialization) all the problem characteristics are defined. In the scenario definition the P-set of possible sites, to locate the stations, is selected and some areas of interest (areas to calculate the system parameters -basically LoS and theoretical accuracy-) are defined. Then, the initial stations sites (normally by a random selection) and all the variables are initialized. The variables can be classified as requirements or restrictions. The requirements are the number of stations (or a range of minimum and maximum number), the horizontal accuracy and the System Probability of Detection (SPoD) [1]. All of these are input data to the problem. On the other hand, the restrictions are the LoS redundancy, which is the minimum number of stations that must cover a point, in the coverage area, in order to satisfy the requirement of SPoD and the minimum spatial separation between the ith and jth station. In this work, we calculate the restriction of LoS

redundancy based on the manufacturer data about the PoD of each station. The SPoD, for a given point j, can be calculated as follows,

where PoD is the probability of detection of one station and it should be provided by the manufacturer and, is the number of stations that cover the jth point. In (2) it is assumed that at least four stations are needed to calculate the position. By (2) it can be estimated the minimum number of stations that make

equal or greater than the corresponding requirement for the SPoD. This minimum value is taken as the LoS redundancy restriction. Moreover, this value also depends on the performance of the location algorithm used and in any case it can be modified (normally increased). However, in the remaining of this work, we assume that the LoS redundancycalculated by the evaluation of (2) also satisfies the location algorithms performance.

In the second step (System Design Evaluation), the quality of the partial design is evaluated. For this, the line of sight and the system accuracy are calculated and these values areintroduced to a fitness function which assigns a suitable scoreand thus quantifies the system quality, regarding to the requirements and restrictions as defined in the first step.

The line of sight calculation is performed only in those points within the areas of interest and the system accuracy is obtained by the CRLB analysis [2] only in those points that satisfy the requirement of LoS redundancy. In this work, the CRLB formulation takes into account also the propagation effects, the instrumental errors, synchronization errors and the analog-to-digital converter sample period and resolution [2]. Unlike to the work presented in [6] which calculates the DOP for arrays of stations, here the CRLB for each point is calculated with all the stations with LoS for that point.

The quality of system design is evaluated and quantified by a fitness function (cost function) that takes into account the set of design requirements, i.e. the technical and economic aspects. The technical aspects are related with satisfying the requirements and restrictions and the economic aspects are related with the number of stations used. This last aspect is useful to those simulations which seek to optimize the number of stations. The fitness function is particular to each problem but, in a general sense the function proposed in this work takes the following form,

where cond is the total number of requirements and restrictions, is the cost of the ith requirement or restriction and is a weight factor that controls the importance of on the design. The corresponding values of and the functions to obtain , for each application, are shown in the next section.

Finally, in the third step (Genetic Algorithm -GA-), a genetic algorithm is used to iterate and to modify the partialsolution which will be evaluated by the iterative procedure


Figure 2. Barcelona airport layout.

described in Fig. 1. The genetic algorithm used in this work is basically the same used in [6]. Therefore, it is not the aim of this work to describe that algorithm. The only difference is that, due to the discretization of the search space to P possible options, here, an individual consists of an -array of integer numbers, where the value of the ith array position represents the index of the selected site for the ith station. Instead, in [6]each individual is composed by the set of (x,y) coordinates of the stations. Moreover, it is worth to say that the information contained in a specific individual position can change and depends on the parameters to be optimized in the design. This particularity is commented in the next section.

III. SIMULATION AND RESULTS

To validate the strategies proposed in this work three different simulations have been carried out over the layout of Barcelona (Spain) Airport. The common objective for all the simulations is to obtain a MLAT system which cover the three runways, the taxiways and the apron centrelines, given a set of requirements and restrictions. The first simulation consists in the design of a MLAT system with a fixed number of Time Difference of Arrival (TDOA) stations. The second one consists in the design of a MLAT system with a variable number of stations. In this simulation, the objective is to find a design that satisfies all the requirements and restrictions by using the possible minimum number of TDOA stations. The last simulation consists in the design of a MLAT system with a fixed number of TDOA and AOA stations. Fig. 2 shows the Barcelona airport layout and the P-set of available sites for the simulations. For these simulations P=41.

For all the simulations, the antenna station height (mast length) has been assumed to be equal to 2 m and the calculations for LoS and CRLB are performed for a spatial grid of 5m 5m. This spatial grid is also in concordance with the Digital Terrain Model (DTM) used to calculate the LoS. The Genetic Algorithms (GA) parameters for all the simulations are those described in [6].

A. MLAT System with a Fixed Number of TDOA StationsThe first scenario shows the first and the standard strategy

proposed herein. It consists in the design of a MLAT system for a given set of requirements and restrictions. The

requirements for this particular simulation are based on those described in [1], which are basically: Horizontal accuracy must be within 3.75 m and the System Probability of Detection must be better than 99.9%. The number of stations to use in this design is twelve and they measure only the TDOA parameter. The restriction of LoS redundancy, using a station probability of detection of PoD=97%, provided by a quick evaluation of (2) is 7 and the minimum spatial separation is = 400 m.

For this scenario, an individual is an array of 12 1 size, where the ith position represents the index of the possible position for the ith station and it can be written as

, where and are elements of the search space, i.e., the P-set of available sites shown in Fig. 2. The fitness function for this scenario takes the following form,

where is a function which quantifies the requirement of total coverage for a partial solution at time t, i.e., the percentage of points that are covered for more than LoS redundancy stations within a horizontal accuracy better than the corresponding value stipulated in the requirements and,

is a function which quantifies the restriction of minimum spatial separation between two stations for a partial solution at time t. These two functions can be calculated as follows,

and

Finally, the value of the weight factors depends on the importance given to each requirement or restriction on the design; they can be chosen by the designer. Here, we have used

=0.95 and =0.05. The only condition that they must satisfy is that the sum of these must be equal to 1. The function in (6) penalizes those solutions with stations close to each other a distance smaller than . However, there exists the possibility to obtain solutions with two (or more than two) stations in the same site. These particular situations are penalized directly in (4) instead in (6). In this way the final expression for the fitness function takes the following form,

where .

Fig. 3 shows the horizontal accuracy for this scenario and how the interested airport areas are covered with the assumed requirements. From the theory [2], [4-5] it is well known that a correct system geometry, to obtain high accuracy levels, is to set the stations in a polygon enclosing the interest area. In Fig. 3 it can be observed that the proposed procedure provides a solution that is in the line of this theoretical aspect. Finally, Fig. 4 shows the procedure convergence. In this scenario, the


Figure 3. Horizontal accuracy for the design with a fixed number of TDOA stations.

Figure 4. GA convergence for the design with a fixed number of TDOA stations.

number of possible combinations, provided by (1), is 7.8987 109 and a relative good solution is obtained within 50 iterations, which means only 500 problem evaluations. However, it is advisable to expend more iterations (up to 200) because the random component of the GA allows to the procedure the exploration of new values in the search space. In any case the total number of problem evaluations is much smaller than that value provided by (1).

B. MLAT System with a Variable Number of StationsThe second scenario consists in the design of a MLAT

system with a variable number of TDOA stations. In this kind of scenario, the objective is not only to calculate the stations sites but it is also to calculate a relative minimum number of stations that satisfy all of the assumed requirements and restrictions. All requirements and restrictions for this problem are those described for the first problem. Moreover, for this problem it is necessary to stipulate a range for the number of stations. For this work, we have used a range of .

For this scenario, an individual is an array of variable length, where the first position sets the length of this. It can be written as , where is the number of stations calculated at time t. The fitness function for this scenario takes the following form,

whereand is a function that quantifies the importance given to the requirement of number of stations. This function is expressed as follows,

Finally, the weight factors values used for this problem are =0.85, =0.05 and =0.1.

Fig. 5 shows the results for the horizontal accuracy. Also in this scenario, all the areas of interest are covered satisfying all

requirements and restrictions. The important aspect in this scenario is that the minimum number of stations calculated is 11, it is, one less station than in the first scenario. This kind of simulation is useful to know an approximate minimum number of stations that meets the requirements and restrictions. However, due to the random component of the GA it is advisable to run the procedure, for this scenario, once or twice more, just to validate the calculated minimum number. Finally, Fig. 6 shows the procedure convergence for this scenario, for this scenario a good solution is found after 150 iterations. It can be understood because the complexity of this problem (number of possible combinations) is much greater than that of the first scenario.

C. MLAT System with a Fixed Number of TDOA/AOA stationsThis scenario consists in the design of an improved MLAT

system with a fixed number of TDOA/AOA stations. Normally, the AOA measurement capabilities are added to improve the horizontal accuracy in surface movement applications [2]. For this scenario the requirements and restrictions are those described for the first problem and the AOA measurements capabilities are added only to the station number 1 (the AOA measurements error is assumed to be 10-3

rad).

For this scenario, an individual is represented as in the first scenario, i.e., as an array of 12 1 size . The difference lies in that, for this scenario, the pertaining LoS coverage of the station number 1 is relatively more important than those of the remaining stations. This particular aspect is introduced in the fitness function as follows,

whereand is a function that quantifies the relative LoS coverage of the station number 1 and it can be calculated as follows,


Figure 6. GA convergence for the design with a variable number of TDOA stations.

Figure 7. Horiztonal accuracy for the design with a fixed number of TDOA/AOA stations.

Figure 8. GA convergence for the design with a fixed number of TDOA/AOA stations.

Figure 5. Horizontal accuracy for the design with a variable number of TDOA stations.

Finally, the weight factors values used for this problem are =0.9, =0.05 and =0.05.

Fig. 7 shows the horizontal accuracy for this scenario. The complexity of this problem is basically of the same order than that of the first one but, here the CRLB calculation has been carried out by taking into account the accuracy improvement provided by the TDOA/AOA station [2]. The final site for this station is shown in Fig. 7 as the diamond. Also for this kind of scenario it is advisable to run the procedure once or twice more. Similarly to the first problem, here a good solution is found after 50 iterations (see Fig. 8).

IV. CONCLUSION

In this work, a set of practical and useful strategies to design and deploy Mode-S Multilateration systems has been presented. These strategies are based on the use of genetic algorithms along with the well-known CRLB analysis. A general procedure to use these strategies is also proposed and it is useful to design new MLAT systems but also to validate

whether a previous system design could be the optimum solution regarding to a set of available resources.

Three kinds of scenarios have been presented. The first one is able to design new MLAT systems with a fixed number of TDOA stations but also to validate whether a final design (clearly before the implementation) can be improved by feasible but not obvious sites changes. The second one provides a strategy to obtain a minimum number of stations which satisfy all the stipulated requirements and restrictions. The third scenario is proposed to design improved MLAT systems, i.e., by using other type of measurements like AOA or RTD. For this third scenario, an example with a MLAT system using TDOA/AOA stations has been presented but, the use with other measurements combinations is straightforward. Finally, it is worth to say that also these strategies can be used together in order to obtain more reliable results, e.g., firstly the second scenario can be used to obtain a possible minimum number of stations that meets all the requirements and restrictions and then, by means of the first scenario, obtain the optimum sites or just to validate that set obtained with the second scenario.

The use of new requirements or restrictions is also possible


only by modifying the corresponding cost function and their weight factors.

REFERENCES

[1] "Minimum Operational Performance Specification for Mode S Multilateration Systems for Use in Advanced Surface Movement Guidance and Control Systems (A-SMGCS)," EUROCAE, 2003.

[2] G. Galati, M. Leonardi, and M. Tosti, "Multilateration (Local and Wide area) as a distributed sensor system: Lower bounds of accuracy," in European Radar Conference, EuRAD, Amsterdam, 30-31 Oct. 2008, pp. 196-199.

[3] H. B. Lee, "Accuracy Limitations of Hyperbolic Multilateration System," IEEE Transactions on Aerospace and Electronic System, vol. AES-11, January 1975.

[4] N. Levanon, "Lowest GDOP in 2-D scenarios," IEE Proc. Radar, Sonar Navig., vol. 147, pp. 149-155, June 2000.

[5] D. J. Torrieri, "Statistical Theory of Passive Location Systems," IEEE Transactions on Aerospace and Electronic System, vol. AES-20, pp. 183-198, March 1984.

[6] I. A. Mantilla-G, R. F. Ruiz, J. V. Balbastre-T, and E. d. l. Reyes, "Application of Metaheuristic Optimization Techniques to Multilateration System Deployment," in Enhanced Solutions for Aircraft and Vehicle Surveillance Applications, ESAVS 2010, German Institute of Navigation (DGON), Berlin, Germany, 16-17 March 2010, p. Session 2B/3.


Correction of systematic errors in Wide Area Multilateration

Jorge M. Abbud, Gonzalo de Miguel, Juan BesadaGPDS-SSR

Universidad Politécnica de Madrid Madrid, Spain


Abstract—This work presents a method to estimate and correct slow time-dependent position errors due to non perfect ground station synchronization and tropospheric propagation. It uses opportunity traffic emissions, i.e. signals transmitted from the aircrafts within the coverage zone. This method is used to overcome the difficulty of installing reference beacons simultaneously visible by all the base stations in a given Wide Area Multilateration (WAM) system.

Keywords- Wide Area Multilateration; Air Traffic Control; ADS-B estimation

I. INTRODUCTION

Due to the performance improvements of multilateration systems, their application range has been extended from short-range applications (airport surveillance) to medium-range surveillance, such as surveillance in Terminal Maneuvering Areas (TMA) [1]. This system has been called Wide Area Multilateration system (WAM). Under this positive performance evolution, WAM becomes a firm candidate to replace secondary radars in the surveillance network for Air Traffic Control (ATC) [2]. Multilateration determines the aircraft’s position by using the time of arrival (TOA) of the signal travelling from the aircraft itself to a network of fixed receivers (base stations). If the signal is properly coded, it is easy to associate the TOAs relative to one single transmission in the different base stations. This is the typical case in ATC, where RF emissions (ADS-B or TCAS) are used. This way, when the system has the complete set of TOAs in all ground stations, the aircraft position can be determined. The trend in the future ATC surveillance is to use ADS-B as the main source of aircraft positioning. But it is still necessary to have a collaborative backup system in order to enhance surveillance integrity [2]. A promising solution is the use of ADS-B ground stations as WAM base stations. Each base station will send the measured TOA together with the ADS-B information to the ATC control center. Multilateration is performed by processing the TOAs [2].

The accuracy of the multilateration position is determined by the errors in the TOA estimates. From a data processing perspective, these errors can be grouped into three main categories [2]: white noise, synchronization issue among ground stations and propagation effects. The first two are

present in any multilateration scenario, although white noise effects in the position determination are not critical for the S/N values usually managed in these systems. On the other hand, although propagation error has not been taken into account for short-range applications, given the distance between base stations in WAM scenarios (up to 20 or 30NM [1]) this source of error has to be considered. This is required in order to preserve the accuracy from suffering degradation along the coverage area (i.e. a low Dilution of Precision).

This characteristic rules out the calibration philosophy to reduce both synchronization and propagation errors, since installing fixed beacons in Line-Of-Sight with all base stations is costly, if not impossible, for large baseline separations. Also, the propagation error has a hard dependence with aircraft altitude. This way, calibrations for on-ground targets are not valid for flying aircrafts.

In order to solve the synchronization issue between stations, GPS-based methods could be used, but these would not reduce propagation errors. Furthermore, a backup synchronization subsystem would be necessary in order to mitigate hypothetical failures of GPS.

Therefore, one solution to this problem is to add a processing subsystem which corrects synchronization and propagation errors simultaneously, by analyzing the signals transmitted by all aircrafts currently present within the WAM coverage (opportunity traffic).

In a recent paper, authors have studied the possibility of performing calibration using opportunity traffic for WAM systems [5]. Figure 1 presents the block diagram of the proposed calibration mechanism. TOAs measured in each station are associated and sent to the central processor which computes the position. This can be either a master station in the multilateration system or a remote station fusing the information of many sensors (this can be the case for ADS-B technology [3]). The first operation is to apply calibration corrections to pseudoranges (for synchronization issues and propagation). Then, target coordinates are determined as if the calibration was perfect, modeling the propagation error as a polynomial depending on distance. The output of this block is delivered as a position determined by WAM system. In order to compensate for slow time variations in the real propagation and calibration conditions, the system has an open-loop

The work has been financed by Spanish Science and Technology Office under projects TEC-2008-06732 y TIN-2008-06742.


control system that modifies the estimated propagation constants, maintaining the system calibrated.

The system selects the targets located at a determined flight level and spread around the coverage area. Then, the system determines their position, as well as the calibration constants, triggering the iterative algorithm from the target coordinates delivered by WAM system and the calibration constants at the output of the averaging filter. Calibration constants are averaged in order to reduce their variance. Once filtered, they are used for the correction of future pseudoranges.

The position determination is done using the proposed mechanism in [6]. It uses an iterative algorithm with the linearized multilateration equation [7]. This algorithm needs an initial position which is determined using the closed form algorithm of [8]. The system can be implemented in TOA form (determining time of emission, cTe) or in TDOA form (eliminating cTe). If cTe is not necessary, the TDOA form is more accurate due to the complete ignorance about the time of emission in WAM. This is the approach assumed in this work. The same method applies to the determination of calibration constants plus position.

Averaging Computation of

calibration constants

Selection of calibration

targets

cΔTi

cΔTim, Km

(x,y,z, cTe)TOD Calibration

K

Position determination

(DTOA or TOA)

TODs

Figure 1. Block diagram of calibration mechanism using opportunity traffic.

This approach uses a linear model for propagation error and a constant error to represent the synchronization error of each base station. Linear propagation models are appropriate for medium distances (around 75-100 km). For longer distances, calibrations based on linear models do not have the required performance. This paper will extend the method proposed in [5] by including second-order propagation models. The inclusion of a polynomial model to correct the synchronization error in each base station will be studied as well.

The paper is structured as follows: section 2 states the problem of position determination with WAM including propagation and clock synchronization errors. Section 3 compares the accuracy of the system using opportunity traffic when propagation effects are modelled through first or second-order model (with respect to distance). Finally, section 4 analyzes the accuracy degradation due to the clock drifts, followed by a mitigation method proposal.

II. CHARACTERISATION OF SLOWLY-VARIANT ERRORS IN WAM SYSTEMS

The aircraft position is determined by means of the time difference of arrival (TDOA) of the signal at the different base stations. As a first step, a method based on hyperbolic location as described in [9] can be used. Additionally, in the presence of error, a gradient method starting from the solution of the previous treatment will be used in order to refine the location [7].

Each base station in the scenario measures the TOA of the signal received from the target aircraft. The TOA of the signal traveling from the j-th aircraft located in (xj,yj,zj) to the i-th base station located in (xi,yi,zi) can be represented by the following expression:

( ) ( ) ( )iie

ji

ji

ji

jii

ji

nTTPc

zzyyxxc

TOA

+Δ++Δ+

−+−+−==

1

1 222τ

(1)

Equation (1) can be rewritten as:

iiej

iijij

i nTTPc

Rc

TOA +Δ++Δ+== 11τ (2)

where Rij stands for the Euclidean distance between the i-th station and j-th aircraft, ΔPi

j represents the propagation error, Te represents the signal emission time, ΔTi is the synchronism error, and ni the white noise random error.

In order to eliminate the signal emission time uncertainty, the aircraft position will be assessed based on the Time Difference of Arrival (TDOA). This means that all available TOAs for a single emission are referenced to the TOA on one of the base stations. Thus, the TOA equation system is now replaced by a TDOA equation-system, with one less unknown, as well as one less equation:

( )

( ) ( ) ( )mimij

mj

i

mjijj

ij

mj

ijmi

nnTTPPc

RRc

TDOA

−+Δ−Δ+Δ−Δ+

−==−=

1

1, τττ

(3)

where the reference base station is the m-th station.

Let us now characterize the propagation error ΔPij. It is the

uniform vertical gradient of atmospheric refractive index that ‘bends’ the signal propagation trajectory and changes the velocity of light, delaying its arrival to the base station. Figure 2 represents this propagation error with respect to the slant range for aircrafts flying at altitudes between 6000m and 14000m AMSL, using the expressions defined in [10]. The base station is considered to be at sea level.


Figure 2. Systematic error (range bias) due to radio wave propagation for a standard atmosphere.

Two relevant characteristics can be observed concerning this propagation error. First, a second degree polynomial seems to be a good fit for modeling the range bias with respect to the distance. Actually, even a linear approximation can prove to be sufficient for short distances [5]. Second, the coefficients of the polynomial depend on the aircraft altitude. The last observation forces to estimate different propagation models as a function of aircraft height. The system divides the height in different layers (with 1-2 km of thickness). For each altitude layer, the system performs an independent propagation calibration based on the aircrafts inside it.

Taking into account the previous observations, equation (2) can be approximated substituting propagation error by a first-order or second-order model versus Euclidean range:

( ) iieijij

i nTTRKc

TOA +Δ+++≈= 11τ (4)

( )[ ] iieijijij

i nTTRKRKc

TOA +Δ++++≈= 22111τ (5)

The method to estimate parameters for both models will be described in section 3.

Now we shall characterize the clock drift occurring in the base stations. In this study, we consider that since the signal emissions are quasi-periodic, a Time Interval Error (TIE) model of a local clock will be used based on the philosophy of [11]. It consists of a polynomial model projecting ahead on a horizon of N points from the starting point with the k-th degree Taylor expansion:

( ) ( )TnwpTnnT

k

p

pp

pi ,!0

1=

+=Δ λ (6)

where n is the sample number, T is the time step, ( ) [ ]kppp ,0,011 ∈≡ ++ λλ the initial states of the clock and

w1(n,T) is a clock noise with known properties. For large values of n, the polynomial component dominates overw1(n,T). Thus, in this study, we shall characterize this local clock with a second-order polynomial without consideringw1(n,T). Expression (6) now becomes:

( ) ( ) ( ) ( ) 222,1,0, 0

2100 TnnTnTT iiii λλλ ++=Δ (7)

This way, TDOA between stations i-th and m-th for a signal transmitted at time t=nT for the j-th aircraft under coverage can now be written in the following ways:

( ) ( ) [ ( )( )( ) ] ( ) ( )( ) ( )mimimjij

mjijj

ijmi

nnnTTnTTRRK

RRKc

nTTDOAnT

−+Δ−Δ+−+

−+==

222

1, 11τ (8)

On the right side, we are now in a position to set a system of TDOA equations in order to determine the aircraft position. On the other hand, the number of unknowns has increased due to the characterization of propagation effects and relative synchronization errors. Therefore, the solution of the system shall not only contain the aircraft coordinates, but also the constants relative to both propagation error and clock drift.

In order to avoid an indeterminate system of non-linear equations, a set of new independent equations must be obtained. For this purpose, the opportunity traffic method will be used.

III. CORRECTION OF THE SYSTEMATIC ERROR DUE TO PROPAGATION

This section focuses solely on the propagation effects and the technique used to solve the equation system without considering clock drifts (synchronization error is considered constant with time).

Since the calibration constants must be determined together with coordinates (three spatial coordinates plus emission time for each aircraft, two propagation constants and one synchronization constant for each base station minus one for the reference station), there is a need to process jointly the TDOA of M ( N/(N-4)) aircrafts for the linear propagation error model to obtain the sufficient number of equations. It is necessary that the number of base stations, N, is greater than 4, the minimum number of pseudo-ranges required to determine spatial coordinates and emission time. The pseudo-ranges of the extra stations are used as equation to determine the additional unknowns of the calibration models. When using the parabolic propagation error model, the minimum number of aircrafts under coverage shall be M (N+1)/(N-4).

A larger amount of jointly-processed aircrafts yield a stronger degree of over-determination, providing therefore higher stability in the estimates. The drawback is that larger sparse matrices and larger amount of data shall be handled.

The non-linear equation system allowing the simultaneous determination of calibration constants as well as the location of the aircrafts is as follows (noise terms are not included for the sake of clarity):


( ) ( )

( )( ) ( )[ ] ( )

( ) ( )

( )( ) ( )[ ] ( )

( ) ( )

( )( ) ( )[ ] ( )

( ) ( )

( )( ) ( )[ ] ( )01100

...

01100

...

01100

...

01100

12

12

211

1,

212

12

22121

21,2

12

112

121111

111,

212

112

21211211

12

11,2

NjNjjNj

jN

jN

jjjj

jj

NNN

NN

RRKRRKc

TDOA

RRKRRKc

TDOA

RRKRRKc

TDOA

RRKRRKc

TDOA

λ

τ

λ

τ

λ

τ

λ

τ

Δ+−+−+

≈=

Δ+−+−+

≈=

Δ+−+−+

≈=

Δ+−+−+

≈=

(9)

where Δλ21(0)=λ2(0)-λ1(0), denoted Δλ21 within the remaining part of this paper represent the difference between synchronization errors in two base stations. Note that it is sufficient to determine the difference between synchronization errors since the measured magnitude is the TDOA. So, in a scenario involving N stations and M aircrafts, the amount of TDOA equations is M(N-1).

One way to solve this system is by using a gradient method [7][8], setting the initial value around the intersection of the hyperboloids. The initial condition is determined as indicated in figure 1. Iteration using the linearized system shall be performed until the convergence criteria based on the accuracy requirements have been met.

The vector composed by the unknowns is as follows:

[]TN

MMM

KKcc

zyxzyxzyx

211,1,2

222111

,,,...,

,,,,...,,,,,,

λλ ΔΔ

=x

(10)

(xj,yj,zj) being the position of the j-th aircraft, cΔλi,1 being the synchronization errors between the clocks of stations 1 and i, and K1, K2 the coefficients relative to degrees 1 and 2 modeling the propagation error effect.

The system in (9) can be solved using the following iteration:

kkk xxx += −1 (11)

The initial value 0x must be set around the true solution. This is done using previous estimations of calibration constants and the position determined for each aircraft by the WAM system before calibration process.

For each iteration process, an estimation of the TDOAs is performed using the estimates of vector 1−kx . The error between the measured values and its estimates is defined as follows:

( )1−−= kk xfτε (12)

where f is the set of TDOA estimates. As an example,

)(1,kj

if x is defined in expression (13) below:

( ) ( )( )[ ( )] kkj

kji

kkj

kji

kkji i

RRKRRKc

f1,

2,1

2,2,1,11, 11 λΔ+−+−+=x

(13)

The differential increment is obtained by solving the first derivative terms of the linearized version of system (9). Thus, expression (12) can also be written as:

kkk xA= (14)

where kA is the gradient matrix of system (9) at the k-thiteration. Matrix A is obtained using the following expressions:

∇

∇∇∇

=

2,1sN,1sN,1sN

24,14,14,1

23,13,13,1

22,12,12,1

RR1000

RR000RR010RR001

A (15)

where:

=∇

A/CNi,1i,1i,1

i,12i,1i,1

i,1i,11i,1

i,1

00

0000

[ ]000=i,10

[ ] [ ]TT 000;111 == 01

[ ]Tijjiii RRRRRRRRA/CA/C NNi,1R ,1,,1,2,12,1,11, ...,,, −−−−=

[ ]Tijjiii RRRRRRRR 2,1

2,

2,1

2,

22,1

22,

21,1

21, ...,...,,

A/CA/C NN

2i,1R −−−−=

( ) ( )

( ) ( )

( ) ( )

T

bi

b

j

bj

ij

bij

bi

b

j

bj

ij

bij

bi

b

j

bj

ij

bij

ji

zzKR

zzR

zzK

yyKR

yyR

yyK

xxKR

xxR

xx

f

K

−+−

−−

+

−+−

−−

+

−+−

−−

+

=∇=

121

11

121

11

121

11

1,

21

21

21

ji,1

Since system (14) is usually over-determined, it must be solved using the minimum mean square error (MMSE) expression:


( ) ( )( ) ( ) ( ) kkTkkkTkk SAASAx ⋅⋅⋅⋅⋅= −−− 111

(16)

where Sk is the TDOA covariance matrix and can be estimated from the S/N in each receiver.

In order to assess the performance of the opportunity traffic method, a hypothetical WAM scenario is simulated. The system here considered is composed by six stations, located in the corners of a square (side: 100 Km) and two in the middle of two vertical sides. The station altitudes are arbitrary, but near sea level (this implies a poor performance in aircraft altitude determination). Six aircrafts are considered for the calibration process, all of them outside the square delimited by base stations 1 to 4 with a height of 10 Km ((x Km,y Km): (-150,60) (100,90) (80,-40) (-120,-70) (20,80) (-30,-110)).

Figures 3 to 6 display the mean and standard deviation of the WAM location error for pseudo-range error standard deviation values ranging between 1 and 10m. The results have been obtained using Monte Carlo experiments, averaging sufficient independent solutions to turn the simulation variance negligible. Results displayed in figures 3 and 4 have been obtained using the linear model for propagation error, whereas figures 5 and 6 show the results obtained using the parabolic model.

1 2 3 4 5 6 7 8 9 1040

60

80

100

120

140

160

180

200

220

240

Standard deviation of pseudorange error (m)

Mea

n va

lue

of W

AM

loca

tion

erro

r in

XY

(m)

A/C 1A/C 2A/C 3A/C 4A/C 5A/C 6

Figure 3. Mean of WAM position error using the order 1 estimation of the propagation error.

1 2 3 4 5 6 7 8 9 100

20

40

60

80

100

120

140

160

180

Stan

dard

dev

iatio

n of

WA

M lo

catio

n er

ror i

n X

Y (m

)



Figure 4. Standard deviation of WAM position error using the order 1 estimation of the propagation error.

1 2 3 4 5 6 7 8 9 100

1

2

3

4

5

6

7

8

9

10

Mea

n va

lue

of W

AM

loca

tion

erro

r in

XY

(m)



Figure 5. Mean of WAM position error using the order 2 estimation of the propagation error.

1 2 3 4 5 6 7 8 9 100

50

100

150

200

250

300

350

400

450

500

Stan

dard

dev

iatio

n of

WA

M lo

catio

n er

ror i

n X

Y (m

)



Figure 6. Standard deviation of WAM position error using the order 2 estimation of the propagation error.

Concerning the performance in the X-Y plane, results show that the parabolic model sacrifices the variance of the location error on behalf of its mean value.

IV. CLOCK DRIFT EFFECT ON POSITION ACCURACY

This section covers the assessment of the dual correction opportunity traffic method, since it considers the propagation effects, as well as the drifts suffered by the clocks placed on each base station. Expression (6) suggests that the TOA measurement induced by this drift varies over time. Figure 7 shows how the errors increase rapidly in a biased way. Therefore, the TDOA estimates generated by the algorithm must consider the behavior of each local clock (more precisely, the difference between drifts).

Consequently, the iterative algorithm presented in section 3 is to be used once again, in order to refine the target location. Since two new unknowns shall be taken into account for each equation of the TDOA system, necessary expansions are to be made in expressions (9) to (16) in order to accommodate the TDOA measurements gathered at different instants.

Besides, the scenario described in section 3 has been upgraded, as local oscillators are modeled with a second-order polynomial along the time axis. The experiments have been carried out considering clock parameters typically used in base


stations (e.g. atomic clocks, with the following coefficients for (6): 10-10, 3·10-11, 10-13).

Figure 8 show how the two-step approach based on the opportunity traffic method is capable of bounding the mean error with a small increase in standard deviation.

0 200 400 600 800 1000 1200-800

-600

-400

-200

0

200

400

600

800

Mea

n va

lue

of W

AM

loca

tion

erro

r (m

)

Time elapsed (s)

1stA/C - X axis1stA/C - Y axis2ndA/C - X axis2ndA/C - Y axis3rdA/C - X axis3rdA/C - Y axis4thA/C - X axis4thA/C - Y axis

Figure 7. Mean of WAM position error obtained without considering clock drift.

0 200 400 600 800 1000 12000

2

4

6

8

10

12

Time (s)

Mea

n of

WA

M l

ocat

ion

erro

r (m

)


Figure 8. Mean value of horizontal position error with the two-step approach.

V. CONCLUSION

This paper presents a collaborative backup system that enhances ATC surveillance integrity. Its cost is relatively low since this system reuses the ADS-B ground stations.

This system is able to mitigate the impact of the propagation effects, as well as the impact of the clock drift effects for a limited period of time without using calibration stations. Cases in point are scenarios leading to temporary GPS unavailability, such as spoofing, insufficient number of acquired satellites or even a failure in the GPS receiver aboard the aircraft.

REFERENCES

[1] W.H.L.Neven, T.J. Quilter, R. Weedon, R. A. Hogendoorn, “Wide Area Multilateration”, Report on EATMP TRS 131/04. Version 1.1 - National Lucht en Ruimtevaartlaboratorium, August 2005.

[2] “SESAR Definition Phase – Deliverable 3: the ATM target concept”, SESAR Consortium, September, 2007.

[3] “EUROCONTROL standard document for surveillance data exchange. Part 12: Category 21, ADS-B messages”, EUROCONTROL, ed 1.1 September 2008.

[4] M. J. Leeson, “Error Analysis for a Wide Area Multilateration System”,QinetiQ/C&IS/ADC/520896/7/19, 2006.

[5] G. de Miguel, J. Besada, J. García, “Correction of propagation errors in Wide Area Multilateration systems”, European Radar Conference 2009 (EuRad 2009), Rome (Italy), September 2009.

[6] G. Galati, M. Leonardi, P. Magarò, V. Paciucci, “Wide Area Surveillance using SSR Mode S Multilateration: advantages and limitations”, European Radar Conference 2005, EURAD 2005.

[7] W. H. Foy, “Position-Location Solutions by Taylor-Series Estimation”, IEEE Trans. on Aer. and Elec. Syst., Vol. AES-12, No. 2, pp. 187-194, March 1976.

[8] G. Strang, K. Borre, “Linear Algebra, Geodesy, and GPS”, Wellesley-Cambridge Press, 1997.

[9] Y.T.Chan, and K.C.Ho, “A simple and efficient estimator for hyperbolic location”, IEEE Transactions on Signal Processing, Vol. 42, No. 8, pp. 1905-1915, August 1994.

[10] D. K. Barton, “Radar System Analysis and Modeling”, Artech House, 2005.

[11] Y. S. Shmaliy, “An unbiased FIR filter for TIE model of a local clock in applications to GPS-based timekeeping”, IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control, UFFC-53, pp. 862-870, 2006.


Multilateration system time synchronization via over-determination of TDOA measurements

Martin PelantAdvanced Programs

ERA a.s.Pardubice, Czech Republic

[email protected]

VojtAdvanced Programs

ERA a.s.Pardubice, Czech Republic

[email protected]

Abstract Sensors of any multilateration system have to be precisely synchronized to achieve accurate position estimation

DOA measurements. The GPS common view principle is frequently implemented in Distributed Time systems and on the other hand additional delays with one time reference in Central Time system. Such systems, however, may not operate effectively if the reference time is not available. This paper provides description together with test results of clock synchronization estimated using position over-determination principles applied to TDOA measurements of well-tracked targets in WAM system (Brno, Ostrava and simulated joint system operated by Czech ANS) with a height of about 5 km and higher. Clock correction factors or delays can then be applied to sensor clock signals, providing accurate tracking of targets such as aircraft, even in the event of GPS failure or jamming.

Keywords - Multilateration, TDOA measurements, time synchronization, over-determination principle.

I. INTRODUCTION

Nowadays Wide Area Multilateration (WAM) systems use for r

Central Time (CT) principle. This means implementation of a time reference into side stations when using Distributed Time which is usually in form of GPS Common View equipment. Such a system requires only minor levels of systematic time error corrections on a Central Processing Station (CPS). Incoming pulse trains are already time stamped (TOA) on each sensor. On the other hand, Central Time uses only one time reference implemented at the CPS. TOA stamping is performed at the CPS to which are all trains forwarded.

Both time synchronization principles are more or less affected by systematic errors of Time Difference of Arrival (TDOA) measurements. The main challenge in WAM system design and deployment is its systematic error reduction (e.g. systematic errors of additional time delays in CT system)

The principle of synchronization/calibration is based on the assumption that all targets are tracked in real 3D space, and the fact that the vector of primary, linearly independent, TOA measurements of thesignal belonging to the target may have dimension greater than three (rather than a real 3D space). The theoretical maximum dimension of TDOA measurements vector is equal to thenumber of stations reduced by one.

A special case occurs when any time reference is missing (i.e. GPS in DT). It this case, the system can be

synchronized/calibrated only from one source respectively from real traffic TDOA measurements with targets of unknown position. There exist two different means of calibration:

Time calibration from over determined TDOA measurements with known behavior in height. Minimal operational requirements are 4 stations and one independent source with known height (this can be also ground beacon).

Time calibration from over determined TDOA measurements of targets with unknown position. Minimal system requirement is 5 stations.

This paper will show results of the second method of time synchronization/calibration. The systematic time errors (of additional delays) together with position accuracy (covariance matrix) and current estimate of clock deviations (TDOA deviations) can be then estimated.

II. OVER-DETERMINATION PRINCIPLE

In operation, the method uses the previously reported known position, for example, the position prior to the GPS failure, and/or the ADS-B signal, Mode-S or Barometric heightreported by mode-C transponder. This position data may be only an approximate location of the aircraft, since the data may not be accurate, or the aircraft has moved since reporting the last position. The position of the aircraft is then calculated a number of times using the TDOA measured from a number ofstations forming target s track, preferably five or more, even though the clocks of the different stations may be slightly out of sync.

To calculate an initial position in three dimensions, at least four reporting stations are required. Or, if altitude is known (barometric reporting from a Mode-C transponder, for example) only three reporting stations may be needed to determine position. In either event, the over-determination technique calculates aircraft position based on different groups of receivers within the overall set. Thus, in the simplest example for a group of four sensors, S1, S2, S3, and S4, the position of an aircraft or other object may be determined by using time-stamp signals from different groups of three sensors. For example, a first position may be determined from S1, S2, S3 sensors, a second position from S1, S2, S4 sensors, a third position from S1, S3, S4 sensors, a fourth position from S2, S3, S4 sensors. Thus, a number of different position values (and thus track values) can be readily calculated.


By merging this position data together, discrepancies in reporting station clock signal values can then be calculated, by determining, iteratively, the clock corrections necessary be applied to make all the position values corresponding one toanother. The same technique can be used in three dimensions using five or more sensors, gathered into groups of four. In the test data presented further, more than 5 sensors were used, with results plotted relatively to the one sensor S1 (whose plot would show a straight line).

Figure 1 is a block diagram illustrating the system of the present invention for calibration of TOA/TDOA measurements of dimension 4 or higher. The block diagram illustrates the iterative process (X axis) over time (Y axis). A number of primary measurements are made, using different groups of sensors, as previously discussed. These positions are then converted to Cartesian 3-D coordinates, and when compared to other such measurements, a clock deviation estimate is made. From these clock deviation estimates, additional delays or corrections to clocks are made, using a Kalman filter, and the process is repeated. With each iteration, the positioning data becomes more coherent, and the clock values are brought into sync using the correction factors.

Figure 1. Block diagram illustrating calibration via over-determination of TOA/TDOA measurements of dimension 4 or higher.

The Primary Measurements are computed as follows: Any TDOA measurements with dimensions equal or higher than four may be used in this example. The following processing can be speed up if the primary measurements are supplemented

can be taken from the prior target tracking process. This will omit analytical conversion from the three selected TDOA measurements into Cartesian 3D. Analytical conversion provides only an approximate position estimate which serves as initial condition for iterative computation of real 3D position, exploiting a whole vector of primary measurements.

Conversion from hyperbolic to Cartesian 3D space is made as follows.can be computed from primary TDOA measurements via analytical conversion or can be updated through primary measurement (dimension 4 or more) associated with an existing tracked target. This block computes the most probable target position (from TDOA) in 3D space according to whole vector of primary measurements and its regular covariance matrix defining position accuracy. Afterwards, it computes the difference between the most probable target position in 3D (estimated in previous steps and converted back to hyperbolic space) and entering primary TDOA measurements (from whole set of primary measurement vectors). This difference is estimate of inferior clock deviations (additional delays) with singular covariance matrix. The difference, including the probability matrix, can be computed with an assumption that dimensions of primary TDOA measurements (in hyperbolic space) are higher than dimensions of 3D (Cartesian), i.e., 3D space is subspace of primary measurement (hyperbolic) space. The system is thus over-determined (redundant).

The Clock Deviation Estimate is calculated as follows. Coalescence of one-minute consequent differences, mentioned above, position in hyperbolic space are made, i.e., the corrections of

are estimated. Every measurement may vary in dimension and it is necessary to have one unified measurement, to which all measurements are transformed. All differences are accumulated into one multidimensional value with Gaussian distribution Additional delay errors (reference

during this one-minute period. Note that coalescent estimates of differences may comprise the final calibration output in some applications, depending on the desired accuracy level and the amount of initial discrepancy in the clock values amongstations.

Correction of Additional Delays and the Kalman filter operate as follows. Update (or correction) of additional delays (reference clock errors) uses Kalman filter feature, which allows the system to model the error trend of an additional delay (error of clock synchronization). Clock errors are then filtered.

These delay values may be applied to the sensor clock values (time stamps) as received at a central station, where time-stamped data values are received from a number of stations to perform TDOA calculations to determine aircraft position. Alternatively, these time delay values may be transmitted to sensor stations to correct or update sensor clocksignals. In either event, the position signals may be continually over-determined in order to verify that the clock signals remain accurate over time and to update or revise the clock correction factors.

In this manner, a WAM system can be kept in operation even without a centrally synchronized clock signal, though the means of over-determination techniques. Such a system and method is important, as modern aircraft tracking systems are switching over to an ADS-B type system, where GPS position signals are self-reported by aircraft to determine aircraft positions in a controlled airspace. If GPS signals go offline or


are jammed, position of aircraft would be difficult to determine, unless legacy radar systems were still employed.Wide Area Multilateration offers a backup solution to ADS-B, but may also be dependent on a GPS clock signal for synchronization purposes. The presented principle allows an WAM system to operate in the absence of a central synchronized clock signal, thus providing an effective redundant backup to ADS-B systems.

III. MEASUREMENTS

The real traffic from two WAM systems, namely Brno and Ostrava, were taken for the purpose of synchronization algorithm design and evaluation. The Brno as well as Ostrava system includes 7 stations. The synchronization method was then evaluated on basis of a Kalman filter which estimates clock deviations from over-determined TDOA measurements at stations 2-7 from the reference clock at station 1. For evaluation purposes, a random number generator generates

to slowly diverge without any synchronization, which corresponds to the real clock behavior. The Kalman filter contains a state vector (current estimate of clock deviations), including its accuracy (covariance matrix) computed from measurements. The difference between "real" clock deviation (from the clock at station 1) and estimated deviations produced by synchronization measurements, which can be found in the status vector, is the key result description. That difference corresponds to systematic errors from primary WAM TDOA measurements and affects all tracked targets.

Note that stations that are close in position to other stations are synchronized much more accurately than other pairs. This effect is caused by the small weight of TDOA coordinates from the base between stations in the calculation of the target position, when a relatively large change in 3D position of the target causes only a small change in TDOA coordinates.

Fig. 2 is a series of graphs illustrating systematic errors of TDOA measurements on sensors 2-7, relative to sensor 1 (not shown) of WAM Brno during the worst time period for synchronization (e.g., least amount of traffic) estimated by the over-determination method, where the Y-axis represents clock deviation in nanoseconds, and the X-axis represents time in minutes. The graphs of fig. 2 illustrate the systematic errors of primary WAM TDOA measurements where clock synchronization on each station is based only on real traffic conditions. The graph elements represent progress of systematic errors and the continuous solid portion "lock up" corresponds to the theoretical limit of 99% percentile. These errors can be interpreted as error for a fixed station after application of the over-determination method. Thus, output data from a fixed station will be affected by this error during given time interval.

Figure 2. Systematic errors of over-determined TDOA measurements on each of WAM Brno stations (related to 1. reference station

shown).

Referring to fig. 2, one can see the TDOA measurement error dependency on traffic density at any given time. The weak synchronization during night traffic from a worst time period for synchronization by the over-determination method isbetween 600th and 660th minute (from 08/30/2010, 1 AM until 08/30/2010, 2AM). Fig. 3 shows this particular traffic interval. The dark trajectories represent lower altitude targets (aircraft) while the lighter trajectories represent higher altitude targets. Only the lighter (higher altitude) trajectories were used for sync, as accuracy in obtaining sync from lower level targets is far more difficult, if not impossible, in some situations.

Figure 3. Traffic during the worst time period for synchronization by over-determination method (from 30.8.2010, 1am until 30.8.2010, 2am). Only red

trajectories were used for sync.

Fig. 4 is a graph illustrating the target altitudes showed in fig. 3, over time, during the worst hour of system operation during the test, in terms of synchronization. Again, the dark lines represent lower altitude targets (aircraft) while the lighter lines represent higher altitude targets. As illustrated in fig. 3,


only targets with an altitude of 4800 meters and above were used for generating sync using over-determination.

Figure 4. by over-determination method (from 30.8.2010, 1am until 30.8.2010, 2am).

Only red trajectories above 4800m were used for sync.

Above described synchronization by over-determination for Brno WAM system was performed also for related Ostrava WAM system. Traffic situation was recorded on both systems simultaneously thus multiple targets appeared in both systems during their over flight. The Ostrava system test was chosen due to different siting and traffic conditions. It provides also possibility to perform test on artificial joint system Morava (partly overlapping Brno with Ostrava system) which results will be showed further.

Fig. 5 presents systematic errors in ns of over-determined TDOA measurements for Ostrava system in similar manner as fig.2 for Brno system. The increasing error trend can be seen

The 99% error curve is not higher than 10 ns which comply with MLAT

requirements.

Figure 5. Systematic errors of over-determined TDOA measurements on each of WAM Ostrava stations (related to 1. reference station

shown).

As previosly mentioned, the test data recorded for both systems can be used to simulate traffic in joint, wider, WAM system called Morava. Morava name is chosen on base of geographical relation between these two regions and their location in Moravian part of Czech Republic, see fig.6.

Figure 6. Situation map of WAM systems Brno and Ostrava.

Fig. 7 represents graphs illustrating systematic errors of TDOA measurements on all Brno and Ostrava stations 2-14,relative to station 1 of WAM Brno. The simulation length was shortened to 7 hours according to high amount of input data which are representing again the worst time period for synchronization (night traffic). The progress of systematic errors increases with decreasing air traffic and vice versa decreases with increasing traffic. The main difference in comparison with stand alone systems is in maximal value of

ns even during the weakest traffic conditions. This result implies previously mentioned dependency between synchronization error and number of sparsely separated stations.

Figure 7. Systematic errors of over-determined TDOA measurements on each of WAM Morava stations, i.e. all Brno and Ostrava stations (related to 1.

reference station


Figure 8. Traffic interval with common targets for Ostrava and Brno system. Only red trajectories were used for sync.

IV. CONCLUSIONS

It was presented and proved on real data that any Wide Area Multilateration system can by synchronized by over-determination method in situation when any kind of clock reference is lost (GPS, beacon, Mode-C). It is required that such a system will satisfy basic prerequisites of sparse sensor

distribution and amount of at least five sensor. Test on two WAM systems, namely Brno and Ostrava proved capability of synchronization from targets when 99% of all synchronization error, deviations from refere ns for system with 7 stations. This property is moreover valid for low air traffic during night. Once these two systems are artificially merged into one system, the 99 % percentile margin is even

Any multilateration system can provide by above presented synchronization method from flying targets sufficient performance in case of time reference lost for necessary amount of time during which all airplanes can land safely and any additional surveillance system can be involved.

REFERENCES

[1] M. Pelant, Estimate of Systematic Errors of A Passive Surveillance System (PSS) According to Emitters with Unknown Position at German Radar Symposium GRS 2002, Bonn, Germany, 03-04September 2002.

[2] M. Pelant et al, Provisional U.S. Patent Ser. No. 61/474,350 for: "Time Synchronization via Over-determined Measurements".


Improvement of Multilateration (MLAT) Accuracy and Convergence for Airport Surveillance

Ivan A. Mantilla-G., Juan V. Balbastre-T.,Elías de los Reyes

Instituto ITACAUniversidad Politécnica de Valencia

Valencia, Spain{imantilla, jbalbast}@itaca.upv.es, [email protected]

Mauro Leonardi, Gaspare GalatiRadar and Navigation Laboratory

Tor Vergata UniversityRome, Italy

{leonardi, galati}@disp.uniroma2.it

Abstract In this paper, we study, evaluate and develop the use of regularization methods to solve the location problem in multilateration systems using Mode-S signals. The Tikhonov method has been implemented as a first application to solve the classical system of hyperbolic equations in multilateration systems. Some simulations are obtained and the results are compared with those obtained by the well established Taylor linearization and with the Cramér-Rao Lower Bound analysis. Significant improvements are found for the application of Tikhonov method.

Keywords-multilateration; regularization methods; localization;air traffic control.

I. INTRODUCTION

Nowadays, Mode-S Multilateration systems are a feasible option to be used in the Air Traffic Control (ATC) technological infrastructures, so much so that the European Organization for the Safety or Air Navigation (EUROCONTROL) published in its report

[1] that these systems will be one of three pillars of the ground based surveillance infrastructure for 2020. These systems exploit the SSR Mode-S(and Mode A/C) signals in order to calculate the position of aircrafts and vehicles in the coverage area. They perform the localization by solving a system of hyperbolic equations based on TDOA technique; the pertaining algorithms run at real time in a CPS (Central Processor System) [2].

In some scenarios, it is common to find a typical problem for the system of hyperbolic equations to be solved; i.e., the coefficient matrix has a very large condition number [3]. This problem is defined in the literature as an ill-conditioned problem and the consequence of this is that, when the system of equation is solved, the solution is not correct or it has a big error regarding to the exact solution. The mathematical interpretation of this problem goes back to the three conditions of Jacques Hadamard [4], namely, the solution exists, the solution is unique and the solution depends continuously on the data. If at least one of these conditions is not satisfied the problem becomes ill-conditioned. On the other hand, the effects of this problem in the multilateration systems accuracy have been highlighted in [5-6].

Some ill-conditioned problems can be also found in other fields as image processing [7], electromagnetic scattering [8] or geophysics [9]. In these fields, this problem has been solved by applying a group of methods called regularization methods. These methods basically convert the ill-conditioned problem in a well-conditions are satisfied. In this paper, the authors study and apply one of these methods to solve the ill-conditioned problem in multilateration systems.

It is important to emphasize that no specific reference in the literature has been found on this topic, with the remarkable exception of that published in [10], which is an application for passive location system with angle of arrival measurements.

II. LOCATION PROBLEM IN MODE-S MULTILATERATION

In Mode-S multilateration (MLAT) systems, a number of ground stations (at least three for 2D or four for 3D) are placed in some strategic locations around the airport or the area to be covered. The system uses the Mode-S transmission and asynchronous transponder (Mode-S) replies as well as the responses to interrogations elicited by the MLAT system. Then, the signal is sent to a CPS (Central Processing Station) where the transponder position is calculated. This calculation is based on the Time Difference of Arrival (TDOA) principle, where the intersections of multiples hyperbolas (or hyperboloids), which have been created with the relative time differences, are computed. Each of these hyperbolas follows the expression shown in (1).

where c is the velocity of light, (x,y,z) the unknown target position (aircraft position) and (xi,yi,zi) is the known position of the ith station (i=1 denotes the reference station). Linearizing (1) by Taylor series expansion [11-12] is the most accepted strategy to solve an inverse problem with the hyperbolic equations, in order to estimate the target position. In the current literature, the solution of this inverse problem has been presented as an iterative procedure in the sense of the Least-Squares (LS) [11-12]. Denoting the unknown target position as

and comprising the system measurements (for a

Mr. Ivan A. Mantilla-G has been supported by a FPU scholarship (AP2008-03300) from the Spanish Ministry of Education.


number of ground stations) in a vector , the final formulation of that method

can be summarized as follows,

where ; is the Jacobian matrix of the hyperbolic equations (1), is the starting point required for this method, and is a vector comprising the TDOA (see (1)) quantities evaluated at the partial solution . Finally, because this method is based on an iterative procedure, K is the number of refinement iterations.

The solution provided by (2) is the minimum residual norm solution and the matrices product is known as the pseudoinverse matrix [3]. For some scenarios, due to the system geometry, to the measurements noise and to the starting point quality, this inverse problem is ill-conditioned and therefore, the solution obtained by (2) is not correct or diverges with very large errors. The numerical reason is because the

conditions [3-4].

On the other hand, one feasible option to avoid the above problem is to use a horizontal projected version of the Taylor-series expansion method and solve it with the pseudoinverse matrix. This option, although the corresponding coefficient matrix is initially well-conditioned, has the disadvantage that it adds a spatial bias due to the projection from 3D to 2D in the coefficient matrix but not in the measurements. As it will be shown in the results, this option normally is more useful for surface movements surveillance.

In this paper, we use the Tikhonov regularization [13] method to solve the iterative procedure of Taylor-series expansion and to avoid those errors due to the ill-conditioned problem.

III. SOLUTION OF LOCATION PROBLEM IN MODE-SMULTILATERATION BY TIKHONOV REGULARIZATION

This method was originally and independently derived by Phillips [14] and Tikhonov [13] and it has been used to solve the ill-conditioned problems in an important number of applications in engineering and science. The main idea of this method is to incorporate a priori information about the size and smoothness of the final solution. This a priori information is in the form of semi-norm. Generally, Tikhonov regularization leads to minimize a function that takes the following form,

where is the exact coefficient matrix for the inverse problem, is called regularization parameter and is called

regularization matrix. The regularization parameter controls the importance given to the regularization term .

Using the Tikhonov regularization concept, the likelihood function [12] for the Mode-S location problem can be expressed as follows,

where is the covariance matrix of the TDOA measurements noise and det denotes the determinant operator. The maximum likelihood solution of (4) is that which minimizes the following function,

Solving (5) by Taylor-series expansion, the estimation for the unknown target position in the Tikhonov sense takes the following form,

where is known in the literature as the regularized inverse matrix of Tikhonov [13] and it is defined as follows,

It is worth to say that, due to the fact that the covariance matrix , for real applications, is often not known because it depends on the true target position, in practice it is common remove it from (7), assuming an identity matrix.

The choice of regularization parameter and regularization matrix is the most critical aspect to make a correct use of the procedure described above. Firstly, the choice of the regularization matrix is directly connected with the statistics of the target position vector . If the components of are assumed to be non-random and uncorrelated, a standard choice of the regularization matrix is , where is a identity matrix.

On the other hand, the choice of the regularization parameter value is not as straightforward as the choice of regularization matrix. In the literature there exist a considerable number of methods and procedures to calculate/estimate an approximated regularization parameter value. These methods provide good results for a variety of applications (e.g. image processing, biologic computer, remote sensing, electromagnetic scattering, etc.) and they are basically based on the solution of an optimization problem, i.e., find a parameter that satisfies some equalities [15] or find a parameter that minimizes some special functions [16-18]. However, it is worth to say that, due to nature of these methods, they introduce a significant computational load and therefore the computation time can be not acceptable for real time location in Mode-S Multilateration.

In this work, we evaluate the problem for several regularization parameters values (no more than three) and then we choose as true solution the one which corresponds with the minimum residual error. This option is feasible for this application because the typical size of the coefficients matrices (Jacobian matrix) is normally smaller than .

In general, the residual error for an inverse problem is given by,


Figure 1. Linate airport system layout. Figure 2. Horizontal accuracy for Linate airport. Each point in the abscissa corresponds to a point in the simulated path.

Remembering that for Taylor-series expansion method, the matrix is an approximation of an exact coefficient matrix, then (8) could not be a correct value for the residual error regarding to the true target position . Therefore, in this work, we propose to calculate the residual error by replacing the regularized solution in the non-linear TDOA function (1), instead in the matrix , as follows,

where the vector is given by,

IV. RESULTS

Preliminary results are shown to validate the improvement of the system accuracy and its convergence by applying the Tikhonov method in the iterative procedure of Taylor-series expansion. Two scenarios have been simulated; the first one is the operating system of Linate Airport (Milan, Italy) and the second one is a multilateration system which is well described and studied by Cramér-Rao Lower Bound -CRLB- analysis in [6].

For each scenario, the horizontal (2D) R.M.S error (obtained via Monte-Carlo simulation with 100 trials), the theoretical accuracy provided by the CRLB [6], the bias of theestimator and the spatial convergence are calculated.

A. Linate Airport SystemThe Linate airport system is composed of eight ground

stations. For this scenario we have simulated a path of surfacemovement around the airport. The system layout and simulated

path are shown in Fig. 1.

For this scenario, the starting point for the Taylor-series expansion method has been assumed to be a fixed point over the airport and it is shown as the star in Fig. 1. For this scenario it has been found that using only one regularization parameter value ( ) is enough to obtain satisfactory results.

Fig. 2 shows the horizontal R.M.S error for the horizontal projection of Taylor-series expansion method and the non-projected (3D) version solved by the pseudoinverse matrix. It also shows the non-projected (full version) Taylor-series expansion method solved by Tikhonov regularization and the corresponding CRLB analysis.

Initially, the CRLB analysis predicts a good accuracy over the entire path, presenting only a few peaks around the points 40 and 50, where the horizontal accuracy is slightly larger than 7 meters. However, for the non-projected Taylor (circles), it can be seen how the ill-conditioned problem avoids the convergence of the method solved by the pseudoinverse matrix, i.e., the R.M.S error tends to infinity in the most of the points. On the other hand, the horizontal projected version obtains acceptable accuracy levels but the effect of the spatial bias is present, for this scenario, in most of the points (more for those points within the 30 and 120). Finally, it is evident how the solution obtained by applying Tikhonov regularization improves both the ill-conditioned problem, which is directlyrelated with the system accuracy and convergence and the spatial bias added for the projected version.

Fig. 3 shows the bias of the estimator for the projected version of Taylor as solved by the pseudoinverse and that one corresponding to the full version of Taylor as solved by the Tikhonov regularization. In this figure it can be noted the improvement, regarding to the spatial bias of the horizontal projection of Taylor-series method, added by using the Tikhonov regularization. This aspect is very important when using tracking algorithms (which are present in all the Air Traffic Control -ATC- systems) because they can improve the R.M.S error of the location algorithm but not the bias. In this way, it is clear to see how Tikhonov method also helps to the tracking algorithms to reach more accurate tracks.


Figure 3. Horizontal bias of the estimator for Linate airport. Each point in the abscissa corresponds to a point in the simulated path.

Figure 4. Spatial convergence for one trial.

Figure 5. Layout of the MLAT system for a takeoff line. Figure 6. Horizontal accuracy over the takeoff line.

Finally, Fig. 4 shows the spatial convergence for a specific Monte-Carlo trial. In this figure it can be observed how the solution by Tikhonov regularization allows the Taylor-series expansion to ensure the convergence to the true point.

B. MLAT System for a Takeoff LineThis system is composed for four stations and it is well

analyzed in [6]. The layout of the simulated scenario is shown in Fig. 5.

For this scenario, the starting point for the Taylor-series expansion method has been obtained by means of the closed form algorithm described in [19]. This algorithm is based on spherical intersections and it does not need a starting point but, as it is shown in the results, it is also affected for the ill-conditioning of the problem due to the system geometry. The horizontal coordinates of the starting point (x,y) are taken from the closed form algorithm and the vertical coordinate (z) is simulated as the barometric altitude, i.e., with a bias of 40m regarding to the real target height. Also for this scenario it has been found that only using one regularization parameter value ( ) is enough to obtain satisfactory results.

The amount of ill-conditioning of this scenario is significantly greater than that of the first scenario. It is because the number of stations here (four) is much smaller than the first one (eight). This effect can be noted in the CRLB analysis shown in Fig. 6 since the theoretical accuracy diverges for points within 0 and 5 km and for those around 20 km. On the other hand, due to the fact that for this scenario, the target height is increasing with the distance, the vertical separation of this with the plane of the ground stations considerably affect the accuracy provided by the horizontal projection of Taylor-series method (crosses) and the spatial bias added by this is considerably large for points beyond 15 km.

Due to the ill-conditioning, it can be observed that, for this scenario, the accuracy levels provided by the full version of Taylor, using the pseudoinverse matrix, diverges far from the theoretical accuracy values (CRLB) for points within 0-5 km and 15-20 km. In contrast, the closed form algorithm presents a more stable accuracy but it is also affected by system geometry (Dilution of Precision -DOP-). Finally, it is evident the significant improvement, of the system accuracy, obtained by applying Tikhonov regularization. The accuracy for this option is stable for the whole of takeoff line and it is not larger than 25


Figure 7. Horizontal bias of the estimator over the takeoff line. Figure 8. Spatial convergence for one trial over the takeoff line.

meters. It is worth to say that this solution is below the CRLB values because also the CRLB is affected by the ill-conditioning of the problem, specifically that part due to the system geometry.

Fig. 7 shows the bias estimator for the solutions obtained by the closed form algorithm and by the full version of Taylor-series using both pseudoinverse matrix and Tikhonov regularization. Firstly, it can be noted that for a few points close to 10 km and 15 km, the bias of the solution obtained by pseudoinverse is smaller (nor more than 1 m) than that of Tikhonov method. It can be explained because in the case of well-conditioned problems the pseudoinverse matrix is the solution with minimum norm [3] and in contrast Tikhonov always adds certain amount of bias [13]. The important aspect is that, if the correct regularization parameter value is chosen, this amount of bias can be neglected regarding to the rest of the options to improve the problem (i.e., the horizontal projection of Taylor-series method). Moreover, due to the ill-conditioned problem, for the rest of the points, the bias added by pseudoinverse matrix solution is infinity and for most of the points the bias added by the closed form algorithm has been found greater than that of Tikhonov regularization.

Finally, Fig. 8 shows the spatial convergence for a specific Monte-Carlo trial; in this figure it can be noted how the regularization of the location problem ensures the convergence also for this scenario.

V. CONCLUSIONS

The implementation of Tikhonov regularization to solve the inverse problem of Taylor-series expansion, for location in Multilateration systems, has been described and evaluated. The theoretical aspects of the method with a practical strategy to calculate the regularization parameter have been described.

For the scenarios simulated here, significant improvements, for the system accuracy and convergence, have been found with the implementation of Tikhonov regularization. For both scenarios, it was found that the regularization of the location problem significantly mitigates the ill-conditioning due to the system geometry, i.e., those points where the CRLB analysis predicts poor accuracy levels; to the measurements noise, i.e.,

those points where the CRLB predicts good accuracy levels but the solution obtained by the pseudoinverse matrix diverges; and also due to the quality of the starting point for Taylor-series expansion method.

For both scenarios it was found that a regularization parameter value of was enough to obtain satisfactoryresults but, it is worth to say that in the situations where the problem is better conditioned, it is necessary to use, at least, one or two more values smaller than , i.e., the smaller the amount of ill-conditioning the smaller should be .

The regularization of the location problem is more useful for those situations where the vertical separation between the ground stations and the target is quite small or for those situations with a small number of stations.

ACKNOWLEDGMENT

The authors are grateful to Thales Italia S.p.A. (Eng. R. Scaroni) who supplied the geometry of the Multilateration system in Linate (Milan, Italy) airport.

REFERENCES

[1] "The ATM Surveillance Strategy for ECAC," Eurocontrol,2008.[2] "Minimum Operational Performance Specification for Mode S

Multilateration Systems for Use in Advanced Surface Movement Guidance and Control Systems (A-SMGCS)," EUROCAE, 2003.

[3] G. H. Golub and C. F. V. Loan, Matrix Computations, Third ed. Baltimore: the Johns Hopkins University Press, 1996.

[4] J. Hadamard, Lectures on Cauchy's Problem in Linear Partial Differential Equations. New Haven: Yale University Press, 1923.

[5] M. Leonardi, A. Mathias, and G. Galati, "Two Efficient Localization Algorithms for Multilateration," International Journal of Microwave and Wireless Technologies, vol. 1, pp. 223-229, 2009.

[6] G. Galati, M. Leonardi, and M. Tosti, "Multilateration (Local and Wide area) as a distributed sensor system: Lower bounds of accuracy," in European Radar Conference, EuRAD, Amsterdam, 30-31 Oct. 2008, pp. 196-199.

[7] M. Bertero, P. Boccacci, G. J. Brakenhoff, F. Malfanti, and H. T. M. v. d. Voort, "Three-dimensional image restoration and super-resolution in flourescence confocal microscopy," Journal of Microscopy, vol. 157, pp. 3-20, 1990.

[8] R. F. Harrington, Field computations by moment methods. New York: Macmillan, 1993.


[9] W. Menke, Geophysical data analysis: discrete inverse theory. San Diego: Academic Press, 1989.

[10] A. Kawalec, M. A. Kojdecki, and B. Wajszczyk, "Regularised iterative estimation of emitter position for passive localisation systems," presented at the International Conference on Microwaves, Radar & Wireless Communications, MIKON 2006, 22-24 May, 2006.

[11] W. H. Foy, "Position-Location Solution by Taylor-Series Estimation," IEEE Transactions on Aerospace and Electronic System, vol. AES-12, pp. 187-194, March 1976.

[12] D. J. Torrieri, "Statistical Theory of Passive Location Systems," IEEE Transactions on Aerospace and Electronic System, vol. AES-20, pp. 183-198, March 1984.

[13] A. N. Tikhonov, "Solution of incorrectly formulated problems and the regularization method," Sovieth Math. Dokl., vol. 4, pp. 1035-1038, 1963.

[14] D. L. Phillips, "A technique for the numerical solution of certain integral equations of the first kind," Journal of the ACM, vol. 9, pp. 84-97, 1962.

[15] V. A. Morozov, "On the solution of functional equations by method of regularization," Sovieth Math. Dokl., vol. 7, pp. 414-417, 1966.

[16] H. Gfrerer, "An a posteriori parameter choice for ordinary and iterated Tikhonov regularization of ill-posed problems leading to optimal convergences rates," Math. Comp., vol. 49, pp. 507-522, 1987.

[17] M. Hanke and T. Raus, "A general heuristic for choosing the regularization parameter in ill-posed problems," SIAM J. Sci. Comput., vol. 17, pp. 956-972, 1996.

[18] G. H. Golub, M. T. Heath, and G. Wahba, "Generalized cross-validation as a method for choosing a good ridge parameter," Technometrics, vol. 21, pp. 215-223, 1979.

[19] H. C. Schau and A. Z. Robinson, "Passive Source Localization Employing Intersecting Spherical Surfaces from Time-of-Arrival Differences," IEEE Transactions on Acoustics, Speech, And Signal Processing, vol. ASSP-35, pp. 1223-1225, August 1987.


Assessing the safety of WAM over a non-radar surveillance area

James Hanson Helios

[email protected]

Ben Stanley Helios

[email protected]

This paper discusses potential issues and solutions for developing an argument that Wide Area Multilateration (WAM) is acceptably safe as a sole means of surveillance in normal and failure-based operations. Using a practical example it discusses techniques to analyse and characterise the behaviour of a distributed receiver and interrogator system. It will also investigate the challenges to gather appropriate evidence, and how they can potentially be overcome.

Keywords: Safety; WAM; multilateration; distributed system; remote sites

I. INTRODUCTION

Under the standards and guidelines currently promulgated by ICAO, EASA and EUROCONTROL, a safety case must accompany the introduction of a new surveillance system such as WAM (Wide Area Multilateration). This requires aproactive approach to understanding risk and applying appropriate controls - i.e. the system being placed into operation must be proven in theory, and subsequently in practice, to meet an acceptable level of safety.

It is important that attention is paid to all aspects of the behaviour of the new WAM system; acceptable safety should be shown when WAM is working as designed, when it has a critical failure (e.g. CPS failure), and when it has no critical failure but, through gradual system degradation, increases risk (e.g. small corruption of position plotted).

The defining case from a risk-based perspective tends to be that of undetected corruption; the gradual degradation of position away from the true value or undetected loss (or failure lack of initiation) of a track. In other words, it is this system failure which has the greatest risk, with the combination of probability of occurrence and severity of credible effects, taking account of all potential mitigations. Whichever way this is caused at an individual component (or function) level, it needs to be identified and mitigated.

A further focus of the analysis can be found in thecalculation of expected failure rates for the provision of surveillance information based on individual components or functions. With the distributed nature of WAM, this is not as straightforward a solution as for other technologies, as it needs to take account of local geometries (i.e. the level of redundancy across the airspace at all altitudes under surveillance-based

control) and the availability of communications and power to each remote site.

This paper therefore discusses potential methods for carrying out a safety case for the installation and operation of a Wide Area Multilateration (WAM) system across an area in which no surveillance has previously been available – i.e. a non-radar airspace (NRA) environment. It is not intended to be comprehensive, but sets out some thoughts on which it is hoped others will build.

II. KEY PROPERTIES OF WAM

Wide Area Multilateration is a surveillance technique for monitoring the position of aircraft with ground-based sensors. As with Secondary Surveillance Radars (SSR) it is acooperative independent technique. The benefits of WAM over radar are typically the lower cost of the receivers and improved accuracy.

A series of ground stations receive the same signal from an aircraft and use the time difference of arrival (TDOA) from each sensor to determine hyperboloids. When plotted from a sufficient number of receivers, these hyperboloids intersect to determine the precise 3D position of the aircraft. This is transmitted as an ASTERIX message (using Category 20 [2] for the target reports and Category 19 [1] for status messages). The position can be further augmented by ranging techniques in which an approximation is made of the time taken for a signal to reach an individual ground station. Integrity of the system can be checked through reference transponders. The reference transponders transmit from a fixed location known to the central processing system and can potentially be used to alert to system inaccuracies.

III. MAKING A SAFETY CLAIM

Before putting into service a WAM system, it is necessary to demonstrate that the system will be acceptably safe by design and in operation. This entails the development of a safety argument that shows how a target level of safety will be achieved by the new system in operation. In practice this involves analyzing the system in two conditions:

a) When everything is working as designed; and

b) When failures have occurred.


The safety claim is made only once it can be shown that the ‘normal’ and ‘failed’ performances of the system are both within tolerable limits.

In terms of performance, a set of reference values have been derived by EUROCONTROL, ostensibly to be independent of the surveillance source (eg the draft Surveillance Performance and Interoperability Implementing Rule). These references values are able to relate certain probabilities of position error (e.g. Y metres at 95% confidence) to the separation minima through the use of a mixture of mathematical analysis and experiential evidence. The latter uses the argument that if radars have been able to provide position accuracy to a certain level, and can support e.g. 5NM separation, then that level of position accuracy is acceptably safe for nominal operations.

The parameters can be simulated and analysed in operational implementation using tools such as EUROCONTROL’s SASS-C (Surveillance Analysis Support System for ATC Centre) [3] and its derivative CAPT (Coverage Analysis and Planning Tool) [4]. Both tools are evolving to handle radar, ADS-B and Multilateration data.

One of the concerns with this approach is that the probability of error values for WAM at other points on the curve (e.g. 98%, 99.5%) may not exactly correspond to the radar error distribution model. At the time of writing, EUROCONTROL and others in Europe are currently responding to this issue through the derivation of surveillance separation error curves, allowing any value to be determined theoretically then tested (or monitored) in practice, and giving a greater degree of confidence in the ability of the WAM system to support a given separation minima (see ED-161 for an example of this approach for ADS-B [6]).

It should be noted that in the absence of CAPT (or other tools), the manufacturers’ models of coverage should be extensively validated through use of flight trials and targets of opportunity. Note the need to look at different flight levels (i.e. the base of coverage) in this process, and also to consider carefully the level of redundancy in the distributed sensors. If a certain performance is required at 2000ft across a wide coverage area (met by 4 receivers and 2 interrogators viewing the airspace), typically greater redundancy will be seen at higher flight levels and therefore the effect of an error in any one of these remote units is minimised.

The driving hazards in a WAM surveillance system typically relate to undetected loss or corruption of aircraft position (assuming the system detects all aircraft within the service volume when working as designed) which, as with any surveillance safety case, would be determined as part of a hazard identification workshop with controllers. Using current safety methodology [5], the next step in the safety case is to examine the failures of the end-to-end surveillance system that could lead to the hazard. This is important in the design and deployment of WAM, as the failure rates of multiplecomponents need to be correlated to understand the failure rate at different points in the service volume. This is not a trivial task in a non-radar environment where no radars exist to interrogate aircraft and the WAM system must actively interrogate to ensure sufficient replies are received to plot the

aircraft position. Over a large area this then means a complex distribution and geometrical spread of installed receivers and interrogators, each one having a specified:

• failure rate (Mean Time Between Failure);

• repair rate (Mean Time To Repair, dependent on not only the maintenance contracts but also the location in the country – e.g. a WAM receiver station mounted on an oil rig may take longer to repair in the event of failure than one nearby a busy aerodrome);

• communications availability (from the remote site to the central processor for WAM);

• power, particularly for environments where a link to a National Grid is not available;

From these parameters, it can be seen that an understanding of the failure rate for different parts of the airspace (e.g. low altitude, high altitude) can assist in determining the communications and power performance to be required, which can be especially important when these are delivered by third party suppliers and service level agreements need to be signed. The communications in particular has the potential to add unnecessary cost if a broad-brush stringent assumption is made instead of an analysis based on operational need carried out.

In addition to the varying reliability of ground components of the system, there is also the aircraft to take into account. For example the reliability of the transponder including the information it is transmitting (with associated human error rates) as well as its trigger level, and overall mean time between failures.

IV. AN APPROACH TO DETERMINING EXPECTED FAILURE

RATES FOR A DISTRIBUTED SYSTEM

As well as taking into account the different failure rates of individual sites it is also necessary to consider the distributed network of possible sites that could potentially be used in any position derivation. One approach is to consider the airspace as a ‘patchwork’ of different probabilities where the likelihood of position determination in each patch is determined by:

• The number of interrogators in view

• The number of receivers in view

• The reliability of each receiver/interrogator

Analysing the failure rate of each receiver/interrogator should take into account the factors identified in the previous section to yield the different failure rates within each ‘patch’. Mathematical analysis of the combinations of failures within each patch can then provide a reasonable estimate of the failure rate of an interrogator and receiver within the patch. Coverage maps (which, as mentioned earlier, would require validation by eg flight tests) can then be used to identify the number of receivers and interrogators available to provide a position (ie avoid the hazard from occurring).

In the following simplified example, the distribution of receivers (Rx) and transmitters (Tx) is indicated by the orange (Rx) and blue (Tx) markers. The distribution is not intended to


represent a recommended siting of receivers and interrogators, but solely acts as an illustration of the approach:

Rx

Tx

Figure 1 The simplified receiver coverage pattern might therefore

look as follows, where a deeper colour indicates a greater overlap between receivers:

Figure 2 The transmitter pattern may conversely look as follows:

Figure 3

Combining these two patterns together provides an overall coverage within the surveillance volume as follows:

Figure 4 Where the different colours indicate the number of

receivers and transmitters in view:

>3 Rx2 Tx

>3 Rx1 Tx

2 Rx1 Tx

1 Rx1 Tx

3 Rx0 Tx

2 Rx0 Tx

1 Rx0 Tx

>3 Rx2 Tx

>3 Rx1 Tx

2 Rx1 Tx

1 Rx1 Tx

3 Rx0 Tx

2 Rx0 Tx

1 Rx0 Tx

Table 1 This can then be used to calculate the probability of

position being lost, corrupted or un-initiated in a particular area of airspace due there being an insufficient number of receivers or transmitters. The defined surveillance volume should ensure that the necessary number of receivers and transmitters are always in view to support the safety objective. This will depend upon the availability of each receiver and transmitter but as the combinations used to derive position are so numerous it may be better to take either a conservative assumption and assume that each receivers or transmitters has the reliability of the most unreliable receiver/transmitter or to mathematically analyse the various combinations to arrive at more accurate reliability for receivers/transmitters in that ‘patch’ of airspace.

V. VALIDATING THE FAILURE RATE AND PERFORMANCE

Having assessed the expected performance and safetyvalues of the implemented system from a theoreticalperspective, the next stage is to validate the operational practice and ensure the safety case remains valid. In particular this should include flight trials and where possible confirmation, through monitoring, of the contributing sensors to position reports.

In addition, integrity monitoring, can be used to provide confidence during operation as a technical mitigation to any tail errors in the position distribution. The distribution of WAM


errors is still being defined but may well be different to the normal distribution assumed for radar (see below)

99.9% 99.9%

Figure 5 Integrity monitoring allows detection of the tails of this

type of distribution using reference transponders or additional receivers (when in view), with the resultant mitigation that the hazard is detected and leads to a lower severity effect. Four receivers are needed to detect position and additional receivers will provide further integrity checks, that when combined with a means of monitoring the contributing sensors can ensure detection and potential elimination of erroneous receivers. Monitoring of the ASTERIX Category 19 ‘status’ messages provide further insight and can usefully supplement integrity monitoring techniques.

VI. OPERATIONAL AND BUSINESS IMPLICATIONS

From both an operational and business perspective it is important to explore the safety case fully. An overly redundant system means extra cost with little added benefit. Insufficient redundancy on the other hand may lead to a reduced surveillance volume due to issues at low level or at the boundaries of the volume. The usual response to the latter issue is a need to re-procure additional receivers or interrogators, with an obvious impact on the investment case.

In the event that receivers/interrogators do fail, the continuity of operations is often an important consideration, particularly for a WAM system placed over a wide area with centralised processing. One mitigation could be to sectorise the airspace taking into account the coverage of the receivers. This may then limit the impact of that failure to a smaller area. For example the base altitude of a sector might be defined at a particular height beyond which there is increased redundancy. Airspace below that sector could revert to uncontrolled airspace in the event of a failure and the controller might only use surveillance below that sector to detect airspace

infringements, whereas there would still be sufficient redundancy in the sector above to support a surveillance service. It is also worth considering the traffic volumes in each sector as lower volume sectors could be allocated a different proportion of the overall safety objective (depending on whether or not traffic volumes have been taken into account in determining hazard severity, in which case they mustn’t be double-counted as a mitigation)

VII. CONCLUSION

A safety case must accompany the introduction of WAM to prove in theory, and subsequently in practice, that it meets an acceptable level of safety. As shown above, it also has a role in enabling the best possible trade-off between the operational requirements (particularly integrity and continuity) and the cost (redundancy). The defining hazard tends to be that of undetected corruption or undetected loss (or lack of initiation) and it needs to be identified and mitigated.

With the distributed nature of WAM, determining expected failure rates is not straightforward and the number of interrogators/receivers in view as well as their reliability should be considered. This will be helped by tools being developed by EUROCONTROL, along with the manufacturer models. However, these do not currently include a full mathematical treatment of the expected failure rates taking account of all aspects.

This paper is not intended to be comprehensive, butnevertheless sets out some thoughts on potential methods for carrying out a safety case for the installation and operation of WAM in a NRA environment. Particular consideration is given to airspace as a patchwork of failure rates that should be validated and monitored. The implications of such analyses on operational and business aspects are also considered.

VIII. REFERENCES

[1] EUROCONTROL STANDARD DOCUMENT FOR SURVEILLANCE DATA EXCHANGE, Part 18 : Category 019, Multilateration System Status Messages. Edition 1.3, December 2010

[2] EUROCONTROL STANDARD DOCUMENT FOR SURVEILLANCE DATA EXCHANGE Part 14 : Category 020, Multilateration Target Reports, Edition 1.8, December 2010

[3] http://www.eurocontrol.int/sass/public/subsite_homepage/homepage.html

[4] http://www.eurocontrol.int/sass/public/standard_page/CAPT.html

[5] http://www.eurocontrol.int/safety/public/standard_page/samtf.html

[6] ED-161 Safety, Performance and Interoperability Requirements Document for ADS-B-RAD Application, September 2009


Implementation of ADS B SystemsBenefits and Considerations


Investigation of Measurement Characteristicsof MLAT / WAM and ADS B


Real Time Performance Monitoring and NoiseAnalysis in an operational WAM System


ADS B via Iridium NEXT satellites


Independent Surveillance BroadcastADS B Receivers with DOA Estimation



Implementation of ADS-B Systems - Benefits and Considerations

Abraham A. Barsheshat Senior System Engineer

Sensis Corporation 5717 Enterprise Parkway, East Syracuse NY, USA

[email protected]

Abstract-- Increasingly, Automatic Dependent Surveillance-Broadcast (ADS-B) is being used around the world as an alternative to Secondary Surveillance Radar (SSR). Implementing ADS-B based surveillance provides many benefits to Air Navigation Service Providers (ANSPs) and commercial aviation. This includes reduced separation and increased capacity; reduced acquisition and life cycle costs; reduced fuel costs and reduced carbon emissions. Additionally, ADS-B reduces the need for maintaining and/or upgrading aging radar infrastructure. This paper describes the NAV CANADA implementation of ADS-B surveillance Over Hudson Bay, the key considerations for implementing these systems and the benefits that were achieved.

I. INTRODUCTION

A. Operational Need The ADS-B deployment in Hudson Bay was the first phase

of NAV CANADA’s multi-phase deployment plan for ADS-B surveillance, as illustrated in Figure 1.

Figure 1 NAV CANADA’s Phased ADS-B Deployment

Hudson Bay covers an area of over 250,000 nm2 with little or no communications or surveillance. Some radar coverage existed over the periphery of the Bay (shown by the green contour in Figure 3). About 35,000 flights a year use this airspace (see Figure 2). The majority of these flights link Europe and North America, while many transit to Asia,

including those using polar tracks. Aircraft traversing the area required procedural separation (80 nm – 10 minutes), which limited the air traffic capacity over the Bay. NAV CANADA’s goal was to improve air traffic operations over Hudson Bay by providing 5 NM separation services over the entire area. This would provide more opportunities to use preferred routes, would increase traffic capacity and would result in significant fuel savings and emission reductions for operators. 5 NM separation services required radar-like surveillance capabilities as well as full communications with aircraft.

Figure 2 Hudson Bay Airspace

B. Possible Solutions

Multiple surveillance solutions were envisioned:

a) Radar: an analysis showed that four Secondary Surveillance Radars (SSR) would be required, at a cost of $10M to $18M per radar site. When adding maintenance costs and installation complexity, it was determined that radar was not an economical solution.

b) Wide Area Multilateration (WAM): a coverage analysis determined that up to 22 sites would be required for WAM. This was determined not feasible because of the lack of sufficient infrastructure, weather limitations and environmental concerns (such as protected lands).


c) ADS-B: ADS-B was a technology that was gaining acceptance among Air Navigation Service Providers (ANSPs) back in 2006. Air Services Australia was deploying ADS-B for Upper Airspace Surveillance and the FAA has plans for NAS wide ADS-B surveillance. The biggest concern was the degree of equipage. IATA was a big supporter of ADS-B as it saw Hudson Bay as an opportunity to accelerate ADS-B equipage among its members. It was determined that the anticipated equipage growth would be sufficient to support surveillance services.

NAV CANADA determined that ADS-B was the most cost effective solution. The business case showed that significant fuel and CO2 emissions savings could be achieved by operators. The decision was made to implement an ADS-B surveillance system augmented by a long range communications system:

• The ADS-B surveillance system would consist of five ground station sites installed along Hudson Bay.

• The long range communications system would include high power (300 watts vs. 50 watts) VHF radios combined with directional antennas to provide longer range. The equipment would be installed at three of the ADS-B sites.

The selected ADS-B sites and resulting ADS-B coverage are shown in Figure 3.

Figure 3 Surveillance Coverage over Hudson Bay

The ADS-B program was executed on a fast schedule:

• Fall 2006: Request for Proposal • Spring 2007: Contract award. The surveillance system

was awarded to Sensis Corporation and the communications system to Park Air Systems.

• Summer–Fall 2007: System delivery and installation

• Winter 2007: Start of operational evaluation • Summer-Fall 2008: Acceptance testing and safety

exemption approval • January 2009: System goes operational

The ADS-B implementation required significant changes to ATC automation and operations, as well as a comprehensive acceptance testing and safety assessment process.

II. ADS-B SYSTEM DESCRIPTION

The implementation of the NAV CANADA ADS-B system comprised multiple facets: (1) installation of a ground surveillance and communications system; (2) modifications to the ATC automation system; (3) modification to ATC operations.

A. ADS-B Ground Surveillance System The ADS-B ground based surveillance network is shown in

Figure 4. This network consisted of remote ground stations and central processing equipment.

Figure 4 ADS-B Ground Surveillance System

1) Remote Ground Stations:

Each of the five ADS-B sites incorporates redundant ADS-B Ground Stations (GS), a RF Site Monitor and satellite communications (Satcom) equipment. Each GS is equipped with a high gain DME antenna that provides a detection range of up to 250 NM. The GS receives and decodes Mode S Extended Squitter (ES) messages from DO-260 or DO-260A equipped aircraft and transmits the decoded messages to a central processor over Satcom. GS redundancy is necessary to provide the required availability due to the remoteness and inaccessibility of the sites for most of the year.

Each GS is equipped with a GPS receiver with Receiver Autonomous Integrity Monitoring (RAIM) capability. The GPS receiver reports a Horizontal Protection Limit (HPL) value that represents the integrity of the GPS solution and can be used to assess the health of the GPS constellation by the automation system. ADS-B reports can be discarded in case of poor HPL.


Each ADS-B site includes a RF Site Monitor that is used for end to end system test. The Site Monitor transmits a known message once a second. Lack of message reception triggers a fault in the ADS-B ground system. The Site Monitor also emulates a radar parrot for the ATC automation system.

2) Central Processing System (CPS):

The CPS is comprised of redundant Target Processors (TP), networking equipment and communications equipment. The TP converts raw Mode S ES messages into ADS-B track reports in accordance with DO-260 and DO-260A. The TP combines target detections received from multiple ground stations into a single output stream. The TP incorporates several mechanisms to eliminate false targets. The TP can also detect and report targets with duplicate ICAO 24 bit Mode S address.

A key NAV CANADA requirement was to make the ADS-B data “radar like” in order to facilitate the integration of ADS-B into the ATC displays. To that end, the TP reports ADS-B tracks by emulating a rotating radar centered at each ground station location. ADS-B tracks are reported in an Extended ASTERIX Category 48 that was created specifically for NAV CANADA. The Extended CAT048 contains target position and identity as well as ADS-B integrity fields such as NUCp (DO-260) or NIC/NAC/SIL (DO-260-A). The standard CAT021 format is also supported but is not currently used by NAV CANADA.

B. Integration of ADS-B into ATC Automation

ADS-B integration into ATC involved several aspects:

1) Radar Emulation: The ATC systems were modified to accept the new Extended ASTERIX CAT048 ADS-B reports emulating a radar rotation.

2) ADS-B Identification: All NAV CANADA ATC systems relied on Mode A code (four digit identity code used by Secondary Surveillance Radars) for target identification and radar track to flight plan correlation, but Mode A code is not yet available from most ADS-B equipped transponders. Mode A code transmission is optional for DO-260 compliant transponders and is required for DO-260A or DO-260B compliant transponders, however DO-260 still constitutes the majority of ADS-B equipage. All ATC systems needed to be modified to use the ICAO Aircraft Identification (ACID) as the target identifier and for association to flight plan.

Note that this requirement adds a small increase to a pilot’s workload: the pilot must ensure that the flight identification entered into the aircraft avionics matches the flight identification on the ICAO Flight Plan (Field 7) in order for the track to be correctly associated to the flight plan on the ATC display. Guidance for proper entry of flight identification has been issued to operators [1].

3) Avionics Qualification/Eligibility Enforcement:Multiple checks are performed to qualify a target for ADS-B services:

a) A notification of ADS-B capability must be filed in the flight plan for an aircraft requesting ADS-B services (Field 18 set to RMK/ADS-B).

b) Aircraft avionics must be certified for ADS-B and must be pre-registered with NAV CANADA [2]. This requires the aircraft to be equipped with a TSO C-129a GNSS receiver and a Mode S transponder with Extended Squitter capability. The ICAO 24 bit Mode S addresses of eligible transponders are entered in an ADS-B Eligibility List (AEL) maintained in the ATC system. Targets that are not on the AEL are deemed not eligible for ADS-B services.

c) Only targets with a NUCp integrity value greater or equal 5 (or NIC for DO260-A/B aircraft) are validated as ADS-B. This corresponds to a 0.5 NM (914 m) containment radius. This exceeds the integrity requirement defined in RTCA DO-303 (or EUROCAE ED-126) [3], which requires a NUCp >=4 for 5 NM separation. It is also worth noting that the GPS performance over Hudson Bay in terms of number of satellites and position accuracy support the RAIM capabilities that are necessary to achieve the required integrity level. The GPS position accuracy that can be achieved over Hudson Bay is depicted in Figure 5:

PDOP over Hudson Bay < 3 SPS 95% User Range Error = 13 m

Position Error (95%) = PDOP x 13m = 39m Figure 5 GPS Accuracy over Hudson Bay

4) Additional changes included handling of mixed SSR/ADS-B traffic, modifications to conflict probes for ADS-B and addition of new ADS-B symbology on the ATC display.


5) A new phraseology was also introduced for the ADS-B airspace. The generic “surveillance” term is used instead of “radar”. As an example, “RADAR SERVICE TERMINATED” was changed to “SURVEILLANCE SERVICE TERMINATED” [1].

The integration of ADS-B surveillance data into the ATC system is summarized in Figure 6.

Figure 6 ADS-B Handling in ATC System

III. REMOTE EQUIPMENT INSTALLATION

The installation of the remote ADS-B and communications equipment required careful planning because of the remote nature of the sites and the limited time window due to the extreme weather conditions during most of the year. Installation planning was done one year in advance. All the required equipment was pre-assembled and tested at MAV CANADA’s Engineering Workshop facility prior to shipment. Existing shelters were used for all Hudson Bay sites. The five ADS-B sites were installed in a 13 week period during the summer of 2007. For each site, shelter equipment installation was completed in about two days and antenna installation took up to seven days. Antennas took longer because of the high gain 1090 MHz antenna size and weight (3.4 m and 27 kg) and tower height (15 m tall). All sites used the community satellite earth link for remote communications. A typical indoor redundant ground station installation is shown in Figure 7.

Figure 7 Indoor Ground Station Installation

The ADS-B equipment has been showed to be highly reliable. No periodic or preventive maintenance is required once the ADS-B equipment is in operation. Failures that are consistent with the equipment’s Mean Time Between Failures (MTBF) have been observed, however these failures did not caused a loss of service because of the fully redundant site design. Failed equipment is repaired or replaced during the summer season.

A typical Hudson Bay ADS-B site during the winter season is shown in Figure 8.

Figure 8 Hudson Bay ADS-B Site in winter

IV. ACCEPTANCE AND CERTIFICATION

NAV CANADA conducted an extensive safety assessment and site acceptance testing program for the Hudson Bay ADS-B system.

The safety assessment was conducted in accordance with NAV CANADA’s Safety Management Process. The goal was to obtain an exemption to Canadian Air Regulation 821, which identifies radar as the sole source of surveillance for application of non visual separation standard. The assessment was completed in two phases: (1) Generic analysis on potential


for technology to meet en-route separation standard; (2) Application in Hudson Bay based on traffic density and complexity.

1) Generic assessment: This included radar to ADS-B comparison and drew on the Australian Safety Case for the Australian Upper Airspace ADS-B program. 20 hazards were identified and mitigated. All hazards could be mitigated to support 5 NM separation standard.

2) Hudson Bay specific assessment: This assessment followed the safety objectives defined in RTCA DO-303 (or EUROCAE ED-126) for the ADS-B Non Radar Airspace (NRA) [3]. The assessment accommodated both DO-260 and DO-260A avionics. It proposed to pre qualify avionics for ADS-B eligibility and identified a ten month operational test and evaluation period. Over 35 hazards were identified and mitigated.

The safety assessment was a two year long process which resulted in receiving the ADS-B exemption to Canadian air regulations in September 2008.

A full week of flight trials was conducted over Hudson Bay including over 20 flight-hours, using test aircraft equipped with WAAS and DO-260A transponders. WAAS data was used as the primary reference. In addition, over two weeks of Target of Opportunity (TOO) data were collected and analyzed. Multiple coverage areas were tested, including radar only areas, ADS-B only areas and overlapping coverage areas. No issues were found when comparing ADS-B to radar. NAV CANADA’s performance requirements were met or exceeded, as shown in Table I.

TABLE I. ADS-B PERFORMANCE

Parameter Results Range 250 nm Update Rate Less than 5 seconds Probability of Update Better than 98%

V. OPERATIONAL IMPLEMENTATION

The Hudson Bay ADS-B system became operational for 5 NM separation services beginning in January 2009. Today, 30 airlines with over 800 ADS-B eligible aircraft operate over Hudson Bay. This represents a 50-60% ADS-B equipage ratio. Priority handling is applied to ADS-B equipped aircraft between FL350 and FL400. Additional improvements to air traffic flow management are planned, starting in October 2011:

• Non-eligible aircraft that are flight planned between FL350 and FL400 will file and fly on fixed route structures, and

• ADS-B eligible aircraft will be permitted to continue to file and fly random (aka preferred or dynamic) routings.

These changes will increase air traffic management flexibility to accommodate eligible customers’ requested tracks, altitudes, speeds and re-routing requests within the ADS-B surveillance service volume, resulting in additional cost savings for customers.

VI. CONCLUSIONS

This paper shows that ADS-B is a cost effective surveillance technology that can provide significant benefits to ANSPs and airlines. A successful outcome requires careful planning that affects all aspects of ATC operations. NAV CANADA has estimated that the ADS-B implementation will save $195 million in fuel and will reduce CO2 emissions by 436,000 metric tonnes by 2016.

Sensis and NAV CANADA were awarded the “Jane’s Environment Award” at the 2010 ATC Global Exhibition and Conference in recognition of the Hudson Bay deployment.

ACKNOWLEDGMENT

The author wishes to thank NAV CANADA for providing invaluable information about the NAV CANADA ADS-B operations. Special thanks to Mr. Dave Ferris, Mr. Mike Botting, Mr. Bill Crawley, Mr. Jeff Cochrane, Mr. Marty Tate, Mr. Will Lynch and Mr. Israel Legault. The author would also like to thank Dr. Michael Raulli from Sensis Corporation for contributing his ADS-B technical expertise to this paper.

REFERENCES

[1] NAV CANADA ADS-B Information Brochure, http://www.navcanada.ca/ContentDefinitionFiles/Services/ANSPrograms/ADS-B/ADS_B_Brochure_EN.pdf

[2] NAV CANADA ADS-B Registration Form, http://www.navcanada.ca/ContentDefinitionFiles/Services/ANSPrograms/ADS-B/Registration_Form_EN.doc

[3] Safety, Performance and Interoperability Requirements Document for the ADS-B Non Radar Airspace (NRA) Application, RTCA/DO-303, December 13 2006.

.


Abstract — For several decades rotating sensors have been

employed as the workhorse for air traffic surveillance purposes.

Therefore, their measurement characteristic is widely

understood. With the adoption of ADS-B and MLAT / WAM,

experiences gained from rotating sensors are no longer valid in

all aspects. This contribution investigates the specific error

characteristics of these new types of sensor technologies. The

goal is to obtain a deeper understanding what specific

characteristics must be expected from these new sensor types.

Index Terms — Multi-sensor data-fusion (MSDF), air traffic

control (ATC), multilateration (MLAT), wide area MLAT

(WAM), ADS-B.

I. INTRODUCTION

For the integration of new sensor technologies in the already existing sensor environment it is of utmost importance to understand the specific characteristics of each sensor, how they interact with each other, and how they interact with systems like a multi-sensor tracker and datadistribution components.

Rotating sensors show well-known and largely understoodmeasurement characteristics like a similar time delay between consecutive measurements. Furthermore, the position biases are very similar for adjacent plots.

The mechanical beam steering enables a significant antenna gain which has a positive impact on the probability of detection (POD), the probability of false alarm, the resistance to garbling, and many other properties related to the high signal to noise ratio (SNR) of rotating radars.

In general, the quality assessment of sensors is achallenging task, because the true position of an object is usually unknown, especially if opportunity traffic is applied for evaluation purposes. However, if new sensors are deployed in an area which is already covered by well-understood sensors, one has the unique opportunity to use the existing framework as a reference while preventing any feedback to the tracking process. To this end, the raw data of ADS-B, MLAT and WAM is compared to raw and tracked data of rotating radars. A discussion on the implementation of multi-target tracker of air traffic control applications is given in [1].

The minimally required infrastructure for ADS-B is a single antenna. For MLAT / WAM a synchronized network of several remote units is required. Typically, such MLAT / WAM receiver units also have ADS-B capabilities.

II. AWPTo analyze the performance of any kind of sensor,

elaborated tools are required to support an operator. The Analysis Working Position (AWP) is an in-house DFS development [2] and can be considered as a necessary complement to the well known SASS-C from Eurocontrol. It puts DFS in a unique position to be able to rapidly adapt to a changing sensor environment.

This analysis tool offers a variety of filtering options and allows for the detection of potentially interesting situations in opportunity traffic. A complete picture on the system performance is basically achieved by a mix between statistical analysis and visual inspection. A snapshot of the AWP is given in Fig. 1. The main window is used for plot and track display purposes. In the lower left a track statistic overview is given. The filter dialog window is shown in the upper right and the replay tool in the lower right.

A. Statistical Analysis of MLAT / WAM and ADS-BIn order to determine the statistical properties of a sensor

like accuracy and POD the plot measurements have to be compared to a reference trajectory. In a multi-sensor environment such a reference trajectory can be computed without utilizing the data under observation to get rid of a correlation between the sensor data under investigation and

Fig. 1 Snapshot of the AWP with its filter dialog, a distinctlypowerful and versatile feature of the AWP.

Investigation of Measurement Characteristics of MLAT / WAM and ADS-B

Klaus Pourvoyeur, Adolf Mathias, Ralf HeidgerDFS Deutsche Flugsicherung GmbH

TM/SP, Systemhaus, Am DFS Campus 7, 63225 Langen, Germany

phone: +(49) 6103 707 4446, fax: +(49) 6103 707 2595, email: [email protected]

plot & track display AWP filter dialog

track statistic overview offline tracker


the reference trajectory. In a single-sensor environment, thetrajectory is necessarily based on the same data due to the lack of other available data [3], [4].

Due to electromagnetic interferences, the accuracy as well as the POD of an MLAT / WAM or ADS-B system depends on the traffic amount present in the region of interest. In case only a limited amount of aircrafts are under surveillance, the probability of detection as well as the probability of false alarm is significantly better compared to high traffic situation within the sensor area of interest. A similar effect can be observed for the plot accuracy.

The data amount depends on the time of day as well as onthe season in the year. During summer time the air traffic amount is, at the same time of day, usually higher than inwinter time. Therefore, the essential influence of the current traffic situation has to be taken into account during evaluations of requirement compliance, e.g. at site acceptance tests.

B. Visual Inspection of MLAT / WAM and ADS-BCompared to a statistical analysis, the main drawback of

visual inspection of data is its lack of completeness. On the other hand, an experienced inspector is capable to detect unknown characteristic within the sensor data.

Potentially interesting candidates for visual inspection areplots which lack specific attributes such as like Mode-Acode, Mode-C barometric altitude, Mode-S address andaircraft identification.

Furthermore, plots which are not associated by a tracker but are located in close proximity to already established tracks are promising candidates for detailed analysis by the user. Such an analysis requires a close coupling between the tracker and the analysis tool. Also, plots without a corresponding track must be considered as false measurements and are of particular interest.

Additionally, tools like the online tracking quality control (OTQC) integrated within the AWP offer the possibility to detect potentially interesting situations like track splits or double tracks. It shall be noted that duplicate Mode S addresses are often related to the erroneous dissemination of Mode S information onto a Mode A/C only track.

III. WAM ERROR CHARACTERISTICS

In contrast to a rotating sensor that measures the round trip delay of the interrogation and the transponder reply, for an MLAT sensor only a reply on itself is required from the transponder. Of course this reply has to be received by several synchronized remote units on different locations in order to perform the multilateration calculation process. Asketch of the MLAT / WAM measurement principle is shown in Fig. 2.

A. Omnidirectional Receiving CharacteristicA common approach for MLAT / WAM systems is to

equip each remote unit (RU) with an omnidirectional antenna, or at least a wide antenna beam, which results in significant structural disadvantages in comparison to a rotating sensor. Due to the lower antenna gain the signal to noise ratio (SNR) is much smaller. Furthermore, a single

Fig. 2 Basic MLAT / WAM measurement principle.

antenna receiver is much more vulnerable to garblingbecause the signals received by the RUs are not limited to a specific direction. A receiver array for a MLAT remote unit overcomes these drawbacks at the expense of a more complex receiver infrastructure. It should also be noted that such an antenna array needs a relative large amount of space. Especially for WAM systems this may result in mounting problems if a multi-purpose antenna tower is utilized.

B. Mode of FlightDue to the omnidirectional reception characteristics of

MLAT RUs the mode of flight has an influence on the quality of the position solution.

Airliners are always equipped with two antennas, one located at the top of the aircraft, one at the bottom side. The antenna which receives the interrogation signal with the higher power sends the transponder reply. Hence for an airliner flying a curve only one side of the aircraft has a good electromagnetic path. RUs on the other side have a structural impairment for detecting the signals covered by noise.

Even if the WAM system is able to interrogate actively,this structural problem cannot be overcome. Therefore for a WAM system it is expected that a curve flight shows a noticeably reduced position accuracy in comparison to a straight flight.

C. Active vs. Passive InterrogationTo keep the electromagnetic interferences as low as

possible, a WAM system may calculate a position solution using only transponder replies initiated by other interrogators. For example, the Frankfurt area is well known to show very large electromagnetic interferences. For such an area a passive interrogation is a suitable countermeasure.

Due to the circumstances that the position estimation is only based on already present signals, a temporal equidistant position solution may no longer be provided by the measurements itself. If the already present interrogation rate of the aircrafts is too low, the system has to initiate transponder interrogation by itself. Only for active interrogation, the round trip delay can be measured directly.A sketch showing active vs. passive interrogation is given in Fig. 3

D. Position Estimation TechniqueFrom an error propagation point of view, the difference

between calculating a complete 3D position and only calculating a 2D position using the barometric height as additional source of information is grave. Therefore it is


Fig. 3 Utilizing different information for calculating a WAM / MALT position solution.

recommendable to use the barometric height of the aircraft in order to reduce the 3D position estimation problem to a 2D problem. The impact on the error propagation is not only given for the variance of the position, it also counts for the bias propagation. A sketch showing the different MLAT principles is given in Fig. 3. A detailed discussion on the error propagation for bias and variance is given in [5].

E. DOP Only for Biased PositionThe dilution of precision (DOP) is a well known concept

to characterize the covariance matrix of an MLAT position solution by a single scalar value. A significant drawback of this concept is the error propagation for the variance which is only calculated for the estimated but not for the true position, which is of course generally not known to the system. Hence the DOP values tend to be far too optimisticespecially if the geometrical circumstances are extremely bad. A sketch of these circumstances is given in Fig. 4.

F. Field of Coverage and Local Position BiasThe position solution of an MLAT system can be

deteriorated by a disturbed detection at a single RU.Especially if the aircraft is close to the ground, the geometry becomes worse. Hence, to achieve a good coverage on lowaltitudes a large number of widely spread remote units is required.

In the border region of the area of coverage it is expected that the performance of the WAM / MLAT system is lower compared to the inner region of the field of coverage. Therefore the sensor area of interest of an MLAT systemalways has to be limited to its core region; otherwise acatastrophic fusion [6] may occur. It should be noted that aremote unit alone is not capable to determine whether the transponder reply is within the area of coverage of a sensor.

Fig. 4 DOP for biased position.

Fig. 5 Basic ADS-B measurement principle.

G. Update RatesWAM systems typically have update rates of 1 Hz up to

4Hz. For MLAT systems the update rate can be as high as10Hz. Conventional interacting multiple model (IMM) state estimators are not able to deal with such high update rates because their model transition process is hampered by the badly observable small effects of target maneuver changes during short time intervals. In [7] - [10] it was shown that the DFS Phoenix tracker is capable of processing such high update rates without a degradation of the IMM model switching process.

H. Mode-S vs. Mode-A/CFor a Mode A/C interrogation only the radar knows which

register was interrogated. From the transponder reply itself,barometric height and SSR code cannot be distinguished.Due to the lack of a unique transponder reply identification by means of the 24 Bit address, the WAM system may calculate a false position based on the replies of several aircraft if no adequate counter measures are implemented by the system itself.

IV. ADS-B ERROR CHARACTERISTICS

For ADS-B, the position information is directlybroadcasted by the aircraft. Hence, only a single receiving unit is required to receive the broadcasted message. A sketch of the basic ADS-B measurement principle is given in Fig. 5.

A. Reception time stampIf ADS-B is broadcasted in the conventional Mode-S

frames, as normally used by radars, the ADS-B message can only be time-stamped with the time of reception, but notwith the point in time at which this message is valid.According to the ADS-B standard, the broadcasted ADS-Bposition may have an age between 0 sec and 0.6 sec. Without compensation, this error results in a velocity dependent bias on the position estimate and additional uncertainty on the velocity estimate. It should be noted that even if the mean value of this time error is compensated by a tracking system and not by the transponder itself, thecovariance representing the inaccuracy of the ADS-Bposition report is increased.

B. ADS-B ValidatedA single ADS-B remote unit equipped with an

omnidirectional antenna is not capable to determine whether the received report originated from a true target or was generated with the intention of spoofing the sensor.


If more than one RU receives the ADS-B message and if these RUs are synchronized, this redundancy can be exploited to validate the ADS-B message with some certainty. This validation can even be done if the number of RUs is not sufficient to calculate an independent position solution based on MLAT principles. Also, an antenna array at a single RU may be suitable to give a rough position validation of the ADS-B report.

C. NUC Values for Ground TracksThe navigation uncertainty category (NUC) values

provided by ADS-B are known to be trustworthy for airborne targets. For aircraft on the ground, two types of erroneous behavior have been observed: first, the NUC value may change to the correct value when the aircraft becomes airborne, second, the ADS-B position may jump to the accurate position. It shall be noted that NUC values on ground can only be 0 or 7 but nothing in between. Therefore NUC values on the ground have to be interpreted differently.

D. Ground Bit not Reliable for AircraftAn aircraft’s mode of flight is determined by a switch

mounted on the nose wheel. Like any mechanical system, itmay fail to estimate the correct airborne status of the aircraft.Therefore, a simple suppression of targets based on this status bit has to be avoided under any circumstances. It should be noted that the ground bit for vehicles is very reliable because it is preprogrammed into the transponder and not based on a mechanical switch. Furthermore, there are different types of ground bits for ASTERIX category 21on one hand and categories 20 and 48 on the other(flight status). These ground bit types can be mapped onto category 62 individually. However if category 20 has been previously converted to category 10 (as is customary for some multilateration systems) it is no longer possible to distinguish between the two types of ground bits.

E. Types of 24 Bit AddressesIn addition to the 24 bit ICAO addresses, ADS-B is able

to transmit other types of 24 bit addresses. Although it is required that aircrafts shall only transmit ICAO addresses, it occurs that aircraft indicate an address of different origin in their ADS-B report which, of course, does not comply withcurrent regulations.

For large airports where a huge number of vehicles are equipped with ADS-B transponders, it is likely that non-ICAO 24 bit addresses are employed, in order to avoid registration fees and possible conflicts with aircraft addresses. It is of course essential that a tracker does not confuse these different types of 24 bits addresses. It should also be noted that the current version of ASTERIX category 62 is not able to represent the different types of 24 bitaddresses that can be transmitted by ADS-B.

V. CONCLUSION

In this contribution the different measurement characteristics of MLAT as well as ASD-B were discussed. It was emphasized where their basic behaviour deviates from the well-known performance of rotating sensors. Another intention of this contribution was to act as a basic guideline

for air navigation service providers (ANSPs) as and sensor manufacturers for designing proper test cases.

REFERENCES

[1] R. Heidger and A. Mathias, “Multiradar Tracking in PHOENIX and its Extension to Fusion with ADS-B and Multilateration,” in Proc. European Radar Conference (EURad 2008), Amsterdam, The Netherlands, Oct. 29-31, 2008.

[2] B. Euler, R. Heidger, et al. PHOENIX AWP Benutzerhandbuch.Version 1.3, Langen: DFS, 2009.

[3] R. Heidger, The PHOENIX White Paper. Version 3.0, Langen: DFS, 2011.

[4] B. Euler, R. Heidger, et al. PHOENIX Systemhandbuch. Version 3.8, Langen: DFS, 2009.

[5] K. Pourvoyeur, A. Stelzer, and G. Stelzhammer, “Error Estimation for Reliable Fault Detection of a TDOA Local Positioning System,” in Proc. Enhanced Surveillance of Aircraft and Vehicles (ESAV’08),Island of Capri, Italy, Sept. 3–5, 2008.

[6] H. B. Mitchell, Multi-Sensor Data Fusion. Berlin: Springer, 2007.[7] H.A.P. Blom and Y. Bar-Shalom, The Interacting Multiple Model

Algorithm for Systems with Markovian Switching Coefficients. IEEE Trans. Automatic Control, vol. 33, no. 8, Aug. 1988.

[8] A. Mathias and R. Heidger, “Design of an Interactive Multiple Model Kalman Filter (IMMKF) in PHOENIX,” in Proc. Enhanced Surveillance of Aircraft and Vehicles (ESAVS 2007), Bonn, Germany, March 6-7, 2007.

[9] H. Binzel, K. Engels, R. Heidger, A. Mathias, C. Klümper, A. Pfeil, R. Cadete, and P. Santos, An IMMKF implementation in the PHOENIX multi-radar tracking system for the Portuguese airspace. Air Traffic Control Quarterly, vol. 16, no. 1, 2008.

[10] A. Mathias and K. Pourvoyeur, “Enhanced IMM Model Switching using Residual Accumulation,” in Proc. Enhanced Surveillance of Aircraft and Vehicles (ESAVS 2010), Berlin, Germany, March 16-17, 2010.


Real Time Performance Monitoring and Noise Analysis in an operational WAM System

Alexander Pawlitzki, Holger Neufeldt Thales Air Systems GmbH

Korntal-Muenchingen, Germany [email protected], [email protected]

Abstract— Wide Area Multilateration (WAM) systems are

complex systems and suffer from several typse of interference

that vary over time. To ensure reliable operation and high data

quality, a continuous verification approach is needed. The typical

quality metrics in Asterix Cat 20 data are not fully sufficient to

determine the operational capability of a WAM system. Different

parameters are needed additionally to characterise system

performance. This approach also allows detecting small changes

and degradations of the system over time that may not only be

caused by system intrinsic effects, but also by external evolution,

like RF environment, different traffic patterns and equipment

mix, or different surveillance infrastructure.

This paper shows crucial low level parameters and their real time

evaluation within the operational system like

propagation path effects (especially for low level

targets)

probability of telegram reception and error rate

interrogation efficiency

accuracy and outlier rate of timing measurements

system synchronisation monitoring

measurement and track noise characterisation

comparison against other metrics (e.g. ADS-B and the

side effects thereof)

aircraft transponder anomaly monitoring

Based on these analyses, optimized data output strategies are

proposed how to convey the maximum amount of information

into Asterix reports for further processing. A more detailed

definition of some fields will become necessary, especially when

the MLAT data is fed into a sensor data fusion.

Keywords: Multilateration, WAM, Noise Analysis, Realtime Performance Monitoring

I. INTRODUCTION

Wide Area Multilateration, or, WAM systems, especially when targeting nationwide systems, consist of tens to probably a hundred independent sensors. All of them form a complex “mesh” network that delivers aircraft surveillance data. While the probability of equipment failure is certainly increasing with quantity, such a distributed system improves its robustness with an increasing number of ground stations so that individual equipment failures can be tolerated (also referred to “n-1” case). The impact of a single failure cannot be determined using an overall performance assessment , notably as the

system is designed to tolerate individual failres. Instead, detailed impact analysis and individual performance measurements are required.

Every single sensor of a WAM system is subject to environmental effects and has an individual RF environment; all of them are not constant over time. While installing a Thales WAM system under contract by DFS Deutsche Flugsicherung around Frankfurt, Germany, many of these aspects where investigated. This system also referred to as Precision Approach Monitor Frankfurt (PAM FRA) provides WAM and ADS-B coverage from Frankfurt airport ground, via CTR and TMA to upper airspace in a volume enclosed in a horizontal rectangle of 128 NM x 80 NM around Frankfurt airport, Germany as illustrated in Figure 1.

Frankfurt/Main Airport

Frankfurt/Hahn Airport

Frankfurt/Main Airport

Frankfurt/Hahn Airport

Figure 1: Horizontal coverage outline of PAM FRA system

Frankfurt is generally recognized as a very busy airspace, particularly also with respect to 1090 MHz radio load.

The Thales system composed of some 30 ground stations deployed at suitable sites around the coverage area, has to cope with Mode S and Mode A/C equipped traffic consisting of a changing mixture of general aviation, military and commercial aviation.

The following sections discuss an approach to system verification that combines classical performance assessment methods with real time monitoring of special system parameters.


II. STANDARD APPROACH

The classical way to assess WAM system performance is to either perform a series of dedicated test flights (which can be a lengthy exercise, particular in a complex coverage environment) or use opportunity traffic provided there are other surveillance systems with similar coverage (e.g. radar airspace). The latter method was chosen for the PAM FRA system.

In any case, standard evaluations verify parameters like Probability of Detection (PD), Probability of Code Detection (PCD), Horizontal Position Accuracy (HPA), averaged over the observation period. The performance parameters are calculated typically by post processing operational system output data (e.g. Asterix category 020 target reports) using suitable external tools (e.g. Eurocontrol SASS-C, DFS Phoenix/AWP, Clairus PerformanceView, Thales TPtool/DART, etc.).

An example of a SASS-C WAM PD evaluation for a 1s update period in the Frankfurt coverage volume based on untracked plot output is shown in Figure 2. It should be noted, that the typical WAM target report timing using untracked plots cannot be equidistant in time due to the asynchronous characteristic of the WAM principle thus creating difficulties for some evaluation tools. Downstream client systems (e.g. multisensor trackers) typically cope with such timing and certainly prefer untracked input instead of pretracked target reports.

Figure 2: Sample PD coverage of the Frankfurt area in range vs. azimuth and altitude vs. range representation captured using Eurocontrol’s SASS-C tool suite.

In order to visualise the data basis of evaluation, trajectory displays and altitude distribution as illustrated in Figure 3 and Figure 4 are often used.

There are, however, considerable effects correlated to traffic density (which varies over day and night, but shows also yearly effects and weather dependant effects due to a changing number of GA targets). These variations affect performance and cannot be captured in single measurements. Most of the standard metrics like probability of detection (PD) or horizontal position accuracy (HPA) are typically averaged over a certain traffic scenario and are thus almost not sensitive for slight changes.

Figure 3:Traffic density seen by the Frankfurt WAM system over a 3 hour period. The rectangle outlines the required coverage area (zoomed on the right).These assessments provide interesting insight into system performance as seen by the operational user and are thus vital for performance verification. (source: Eurocontrol)

Typically, WAM characterisations are done by post processing so that the results are not available and are not tracked in real time during system operation. In fact, the only real time monitoring in most installations is based on verifying the stationary position accuracy of test transponders. This is certainly a useful but not comprehensive metric, not representing performance throughout the entire coverage area.

Position accuracy is typically calculated as Root Mean Square (RMS) value. This value of RMS can be misleading as it is not defined in an environment which contains different noise types and outliers. Depending on the filter and calculation method, this value changes considerably while the overall quality of the data remains the same.

Figure 4: Trajectory altitude distribution during a 1 hour sample of the PAM FRA coverage volume rendered by Clairus PerformanceView

Looking at high level performance parameters thus provides some insight into the operational performance of the system as a whole, but does not allow assessing system reaction to varying environmental conditions, nor does it allow understanding root causes of certain reactions. More detail and different parameters are therefore needed.


III. ASSESSMENT USING REALTIME MONITORING METHODS

A WAM system operator needs confidence, that the system is able to deliver its specified performance at any time, and he needs to be notified, as soon as this is no longer the case – for whatever reason.

Notably detecting slowly evolving, slight degradations before they become relevant for performance is an important strategy to ensure continuous reliability of such complex systems.

During the installation of the PAM FRA system, new techniques were tested that measure these effects at different test points throughout the processing chain. This allows characterising the whole data flow and check efficiency rates at major processing steps and also to monitor those values under different scenarios. It can be seen, that the different stages in the processing have different sensitivities to the environmental effects. Special attention was given to obtain all this information from measurements and signals that are used anyhow for system operation, so that no additional loading of the RF environment occurs.

A. Reception Efficiency Measurements based on Synchronisation The basis of these measurements was the redundant

synchronisation concept combining GPS-based timing with an RF-based time beacon transmitter network. The latter is an evolution of the reference transmitter concept known from other installations; instead of transmitting transponder-like signals by dedicated reference transponders, signals with a dedicated and special payload were used. These are transmitted by receive/transmit ground stations that are also used to selectively interrogate targets.

In addition to the usual identification information the time beacon messages also contain transmission timestamps, sequence numbers, and status data of the respective transmitting node, so that they can be decoded by the receiving node without the need of an additional data link. The receivers can even belong to other, adjacent WAM systems.

Each node is able to decode these time beacon signals on its own and gain information in real time about reception efficiency (packet loss and packet error rate), synchronity (comparing its own system clock to a multitude of remote time keepers), and, propagation effects (by analysing the synchronisation noise) as depicted in Figure 5 and Figure 6.Additional corrections can then be performed on the level of the central processing station.

It was found, that reception noise was never purely Gaussian. Instead, it was a composite of several noise types with different properties. Typically, if was found:

Gaussian noise caused by statistical measurement error

Flicker or wander effects caused by slowly drifting (synchronisation, thermal effects, propagation effects)

Outliers, caused by undetected garbling of RF signals (strongly correlated with RF environment loading)

Constant offsets, ideally removable by careful calibration

Analysing these different noise types, the environment and its changes can be seen and the processing logic is then able to take these effects into account.

Figure 5: Raw measurements (single shot) of node synchronisation difference vs. time of day. It is clearly visible, that the measurement noise between day and night is different; the number and distribution of outliers also varies. Since the measured signal was a Mode S DF18 telegram, the same noise is present in datagrams received from aircraft. Long term monitoring is then able to reveal trends in system performance.

In the above proposed approach, reception efficiency can be directly measured the at each site and at various signal power levels. The possible contribution of this node can be determined permanently in real time.

Figure 6: Measurement noise (red dots) and signal power (green dots) for a time beacon signal. The plot shows clearly day-night variations (the portion during the night with lower traffic density shows less measurement noise), but also strong signal power variations due to weather effects (winter ducting).

In addition to these reception efficiency measurements based on time beacon signals, the results of GPS-based synchronisation can be used as well for analysis: GPS sometimes suffers from multipath due to non optimal antenna location. This effect can be monitored in the station directly as shown in Figure 7 and Figure 8.


Figure 7: Good GPS synchronisation noise measured for Gedern VORTAC site Above GPS sync error signal (Scale: y-axis 10ns/unit, x-axis: 1day/unit). This site provides a nearly optimum signature due to a good GPS antenna location mounted to the side of a fibre glass mast.

Figure 8: Strong GPS synchronisation noise measured for Feldberg site. (Same scale Figure 7, top: GPS sync error signal, antenna installation below). This site provides a noisy signature due to heavy multipath effects (GPS antenna mounted at the sife of a metal transmission tower)

B. Interrogation efficiency Measurements Once reception efficiency is determined, interrogation

efficiency is another important parameter to characterize a WAM system.

All interrogations of targets can be evaluated to built up interrogation efficiency statistics. They do not only show the general ability to interrogate targets, but also if single targets do not behave nominally or if the interrogation efficiency is temporarily or permanently lower than expected. All this affects the reliability of a WAM system, even if the root causes can be outside the system itself.

Interrogation efficiency is analysed statistically but can be visualized as well as illustrated in Figure 9.

Figure 9: Single Transmitter Interrogation efficiency of GICB extractions around Frankfort for low flying aircraft (below 5000 ft MSL). Green dots are localized positions, red crosses are successful GICB extractions. Most of the ‘green’ traces are aircraft below the coverage limit; in this area, airspace C EDDF is “underflown” by GA traffic with 1500 ft MSL max. By maintaining such data, a low boundary of the coverage volume can be monitored its change can be evaluated versus traffic and time.

C. Noise Analysis of ADS-B Targets of opportunity Another real time monitoring can be performed against

ADS-B opportunity targets. This is not only a useful reference if no other surveillance means (e.g. radar) are available. ADS-B can under certain conditions be used as a position reference with much higher accuracy and update rate than radar.

Since WAM and ADS-B have different types of intrinsic errors, the comparison has to be made carefully. By separating both errors, a cross verification is possible and bias values, which are not measurable by processing the own data alone, can be estimated.

The following examples are taken from an ADS-B vs. WAM verification campaign jointly done by Eurocontrol and DFS and determine position deviation.

An attempt was made to compare ADS-B positions taken from certified ADS-B equipped aircraft against WAM position results. Due to expected issues with ADS-B position reporting latency, the position statistics was done separately along track (Figure 10) and across track (Figure 11). The result did not show


the WAM position measurement noise but a mixture of other effects.

Figure 10: Noise distribution of the along track error WAM vs. ADS-B without ADS-B latency compensation calculated using SASS-C. Span of x-axis is 150 meters. Clearly this is a non-Gaussian distribution, which points to effects beyond simple measurement noise. Finally we see here ADS-B latency variation of the transponders; the noise here is not the WAM noise. (source: DFS)

Figure 11: Noise distribution of the across track error WAM vs. ADS-B calculated using SASS-C. This signature shows a granularity effect, which is the modelling/ calculation resolution inside SASS-C. Scale (full x-axis) is 150 meters. (source: DFS)

Due to this limitations, another tool was chosen (Eurocontrol: D. Lambers / J. Steinleitner) to better model transponder latency and get rid of SASS-C granularity issues. The results are presented in Figure 12 and Figure 13.

Figure 12: Noise distribution of the along track error WAM vs. ADS-B including ADS-B latency compensation. This fits better to a Gaussian

distribution (same scale as before), but it is obvious, that some non-Gaussian effects are still remaining. After some analysis, it turned out, that a deterministic shift in the time tagging was overlaying the measurement. (source: DFS)

Figure 13: Noise distribution of the across track error WAM vs. ADS-B. This is nearly a Gaussian shape; so the underlying noise seems to be measurement noise – physically we see the WAM system here. The RMS here is well below 10 meters. (source: DFS)

As a conclusion of this exercise, it is important to note that using tools with systematic limitatons (granularity) or using references with systematic errors prevent the analysis from seeing the WAM performance itself. Looking at the distribution functions gives a hint, if additional error sources have to be considered.

IV. PROPOSED MODIFICATIONS IN ASTERIX CATEGORY 020The WAM processing chain typically outputs Asterix Cat

20 target reports. These contain many data items but lack sufficient detail of measurement characteristics for downstream client systems (e.g. multisensor tracker) to better consider the quality of individual reports.

It is therefore proposed – similar to the existing ADS-B metrics – to embed more information into Cat 20 reports like:

Track noise (current measurements within the track)

Noise values in current observation interval

Confidence values (not all measurements have equal weight)

Separation between noise and uncertainty

With these additions in place, multisensor tracking of WAM data can be significantly enhanced.

V. CONCLUSION

In addition to the directly operationally relevant performance parameters that are typically evaluated during system acceptance testing, other measurement parameters can provide useful insight in system behaviour under changing environmental conditions. These have the potential to detect trends and issues before performance is actually affected and can thus serve both as early warnings and as root cause analysis for observations.

Additional data items as part of the standard target report message would make available the statistical properties of each


measurement for downstream client systems and thus improve the overall system performance.

VI. ACKNOWLEDGEMENTS

The authors wish to express their gratitude to D.Lambers/J.Steinleitner/S.Adamopoulos (Eurocontrol) and S.Stanzel (DFS) for the great support analysing WAM and ADS-B tracks.


ADS-B via Iridium NEXT satellites Paolo Noschese, Silvia Porfili, Sergio Di Girolamo

Thales Alenia Space Italia SpA, Rome, Italy

Abstract - Iridium NEXT will replace Iridium’s current satellite constellation, ensuring continuity of the first generation of global mobile satellite services. Iridium NEXT constellation will consist of 66 cross-linked, LEO satellites in six orbital planes intersecting over the North and South Poles. Each Iridium Next satellite will integrate a secondary payload. This paper is based on the feasibility study to embark a dedicated ADS-B secondary payload, on board half or all satellites.

Keywords: ADS-B communication, IRIDIUM NEXT, ATM Satcom .

I. INTRODUCTION

The constellation deployment will be completed between beginning 2015 up to mid 2017, when the Iridium NEXT constellation will be fully operational. Iridium NEXT is designed to accommodate secondary payloads and provisions according to specific constraints in terms of volume, consumption, dissipation and electrical interfaces.

The system here presented is based on the acquisition by the Satellite of the ADS-B aircraft signals, namely the 1090 Mode S Extended Squitter, without any change on theaircraft equipment.

II. SYSTEM REQUIREMENTS

Today, the existing ADS-B traffic receiver type AS680-GS (see figure below) perform the ground reception (de-garbling) of the ADS-B signal.

Figure 1. ADS-B ground receiver Thales ATM type AS680-GS

In order to cancel interference induced by other 1090 ES signal (TCAS, response to Mode S radar request, …), it appears as necessary to perform the initial ADS-B reception from the payload. This induces to “spatialise” the ADS_B receiver functions.

The “Spatialisation” of the ADS-B receiver functionimplies a trade-off analysis between Payload and GES (Ground Earth Station):

� Trade-off have to be performed in order to determine the best distribution of ADS-B receiver functions @ the payload architecture,

� Satellite => GES data link.

The Iridium NEXT Constellation is constituted by 66satellites at 780 Km altitude, in 6 orbit planes of 11 spacecraft each, with 86.4° inclination.

Figure 2. ADS-B conical coverage definition, up to 63° off-nadir (TAS-F source).

The baseline link budget hypotheses require for the on board satellite antenna a gain ranging from 2dBi (@ 0° elevation, i.e. the edge of coverage) to -3dBi (@ 63° elevation, i.e. Nadir direction towards the Earth center).

III. MISSION ANALYSIS

First step of the study consisted in the definition of mission and system requirements. Then the mission analysis has been carried on, by simulating the trajectories of both Iridium NEXT satellites and aeronautical users. A set of representative scenarios has been predefined for the mission analysis. In particular,

• Four constellation configurations have been envisaged, in order to explore the possibility to embark the ADS-B payload on board the IRIDIUM satellites:

o Full Constellation: considering all 66 satellites;


o Half Constellation: two different sub-sets of the IRIDIUM constellation have been simulated with the ADS-B payload on 33 satellites:

� N1 (All six IRIDIUM planes have satellites equipped with ADS-B payload unit);

� N2 (Plane A, E and C satellites are equipped with the ADS-B payload unit).

• For each of these IRIDIUM configurations, four different aeronautical routes have been analyzed (Fig. 3) in order to cover the most important and critical situations for the European Civil Aviation Fleet:

o the North Atlantic Route (Case A),

o the South America to/from Europe Atlantic Route (Case B),

o the Europe to/from South of Africa Route (Case C)

o The Europe to/from Far East via Remote North Polar route (Case D).

Figure 3. Selected Air routes to perform ADS-B mission analysis

The expected coverage to be developed by the ADS-B antenna on board to the Iridium satellite shall be a conical pattern type with a field of view up to 63° off- nadir max (Fig. 2). In order to verify if the on board satellite antenna can comply with the stringent link budget constraints and with the payload performance requirements in terms of aircraft-to-satellite loss of contact time (i.e. outages), three different options for satellite elevation angles (θ) have been simulated:

• Full coverage (0° θ 63°),

• Medium coverage (13° θ 63°),

• Reduced coverage (26° θ 59°).

In addition, the performance in the four routes have been simulated against three timing constrains: the total Aircraft to Satellite Contact Time shall not be interrupted more than 15 minutes, 10 minutes or down to 1 minute. Clearly, the best performing IRIDIUM configuration is the FULL capability

with all satellites equipped with an ADS-B payload, at the cost of having to equip 66 satellites with the ADS-B payload. Following table show the results for the IRIDIUM configuration N2 which presents the most promising performance with only half IRIDIUM constellation equipped with ADS-B payload in all three options for satellite elevation angle ranges.

TABLE I. HALF IRIDIUM CONSTELLATION N2 PERFORMANCE

Case A Case B Case C Case D Coverage Time [%] 99,5 81,62 83,35 99,99 Visible Satellites 5 3 2 6 Outages @1 minute 1 20 15 0 Outages @10 minutes 0 3 5 0

Ful

l C

over

age

Outages @15 minutes 0 1 1 0

Case A Case B Case C Case D Coverage Time [%] 99,01 81,1 82,74 99,54 Visible Satellites 5 3 2 6 Outages @1 minute 0 3 4 0 Outages @10 minutes 0 1 1 0

Med

. C

over

age


Case A Case B Case C Case D Coverage Time [%] 57,88 40,68 44,2 78,79

Visible Satellites 2 1 1 3

Outages @1 minute 35 38 35 41


Red

. C

over

age


Furthermore, in order to support the above results, the analysis has been extended worldwide. Two major results are presented in the following figures for N2 configuration (in case of medium coverage angles) in terms of:

• Coverage rate (i.e. the ration of time with at least one satellite in view);

• Maximum Revisit Time (i.e. the gap of time between any of two satellites in view).

In accordance with the performance obtained with the pre-selected Air Routes.

Figure 4. Coverage Rate with Half IRIDIUM constellation N2 for aircraft position worldwide (medium coverage)


Figure 5. Maximum Revisit Time with Half IRIDIUM constellation N2 for aircraft position worldwide (medium coverage)

Final considerations on the three simulated IRIDIUMconfigurations are summarised hereafter:

• Full IRIDIUM constellation is the best performing configuration that has been analysed in this report. On the other hand the number of needed ADS-B payloads is maximised with evident cost issues.

• Half IRIDIUM constellation N2 is potentially the best performing half constellation configuration that has been analysed in this report.

• Half IRIDIUM constellation N1 performance results would lead to conclude that the benefits of this configuration are quite limited with respect N2 configuration also bearing in mind that at least six launches are needed to complete the ADS-B N1 configuration (as the 33 satellites occupy all the 6 IRIDIUM planes).

Now, for the selected IRIDIUM configurations, some additional considerations are provided to explore potential risk of saturation of the space receiver. For our purposes, the processing of ADS-B over high-density terrestrial area must be possible with Instantaneous Aircraft number over 1000 aircraft (this value has been considered as the maximum number of possible aircraft to be processed by one reception channel).

With the average number of simultaneously availablesatellites over the entire coverage interval for the set of ADS-B configurations (Figure 6 shows the result for N2 case), and by considering the estimated the PIAC1 value (Peak Instantaneous Aircraft Count) over ECAC (European Civil Aviation Conference) Area in 2025 (i.e. 8119, taken as input from ANTARES Project), the risk of saturation of the reception channel is identified.

Figure 6. Selected Average number of Satellites simultaneously available over the coverage interval for IRIDIUM full constellation (Full coverage)

Additionally, by considering the possibility to have a multi-beam antenna design (at the cost of processing in parallel all received beams, i.e. one reception beam = one ADS-B processing channel), as described in following section, a substantial minimisation of the risk to overload the reception channel is introduced when tracking more than 1000 aircraft in the same beam.

Accordingly, following table summarises the resultsobtained by considering such a multi-beam antenna design for ADS-B payload, in terms of PIAC vs. Number of available satellites, for the three constellations in case of full and medium coverage.

TABLE II. MINIMISATION OF THE SATURATION RISK WITH A FOUR ELEMENT PHASED ARRAY ANTENNA (4 OR 8 BEAMS)

PIAC vs. Number of available satellites with 4 or 8 beam Phased array antenna

IRIDIUM ADS-B configuration Full coverage (4/8 beams)

Medium coverage (4/8 beams)

Full IRIDIUM constellation 1015/508 1015/508Half IRIDIUM constellation N1 2030/1015 2030/1015Half IRIDIUM constellation N2 2030/1015 2030/1015

IV. ANTENNA DESIGN

The following antenna typologies have been analyzed in full wave (3D EM CAD Model), with the aim to guarantee the link between Iridium S/C and any Aircraft captured in the conical field of view of the ADS-B antenna:

• Single element, i.e. Quadrihelix; • 7-element array antenna. • 4-element array antenna;

Accordingly, the antenna patterns have been shaped, as much as possible, in order to cover with the highest gain possible the required mission conical field of view, without overcoming the stringent requirement relative to the maximum volume allowable on the IRIDIUM satellite as shown in following Fig. 7.

1. PIAC estimation from ANTARES is available for four futurepotential scenarios: Scenario A (High Air Traffic Growth), B and C(Medium Growth), D (Low Growth). The PIAC value from Scenario A isused for the analysis in order to assess a worst case for saturation.


Figure 7. Iridium S/C, ADS-B antenna max volume allowable

A. Quadrihelix antenna Three Quadrihelix antennas configurations (“A”, “B”

and “C”) have been analyzed. The antennas have beenaccurately designed in order to achieve a shaped pattern (Fig. 8) with its maximum in side the medium coverage angle range. However, in order to achieve a reasonable gain for the mission, the antenna length is such that it violates the maximum allowable height of 300 mm.

Figure 8. Antenna patterns relative to A, B, C quadrihelix antenna configurations

B. 7-element array antenna 6 elements run over the periphery of a circular ground plane with a diameter of 400mm while a 7th is located at the centre of the ground plane. For this antenna configuration, the crossed dipole antenna has resulted in being the best performer.

Figure 9. 7-element array antenna Full Wave Analysis

However, this solution is quite demanding in terms of mass, complexity and cost. A good compromise between cost and benefit would be the 4-element option, as described in the following sub-section.

Figure 10. Crossed-dipoles 7-element array pattern

C. 4-element array antenna A 4-element array with a ground plane of 400 mm of

diameter has been designed with the aim to mitigate as much as possible the following important criticalities:

• mass;

• overall array losses;

• array complexity together with its beam forming network;

• use as much as possible of flown and proven technology in order to reduce the development cost;

Based on the above analysis, the preferred baselineconfiguration is the 4-element array with a circular ground plane with a diameter of 400 mm that does not exceed the stringent max allowable volume. The proposed antenna configuration allows a very efficient and compact feeding

MAX Volume allowable: 400mm (L) x 700mm (deep) x 300mm (H)


network with an expected overall loss less than 0.2 dB. In addition, the antenna allows the use of a flown and proven technology developed in the frame of previous domestic Space programs (Sicral, S1B). A full wave analysis is highlighted in the figure below with the antenna layout and the antenna surface currents generated by a dedicated set of elements excitations.

Figure 11. 4-element array antenna Full Wave Analysis

The achieved antenna pattern based on 4-element helix array is here there after shown.

Figure 12. 4-element array pattern

As follows, a preliminary hypothesis of phased array has been also addressed with the aim to provide a suitable solution vs. the receiver saturation in case more than 1000 Aircraft are beating at the same time at the RX front end. In order to make the solution as simple as possible, the same 4 el. Array developed to generate the required conical pattern between 13° and 63° off Nadir has been used to characterize the phased array.

The proposed phased array shall be considered constituted in general by three main sections: • Radiative section; • Active section, in which the required MMIC LNA,

Phase shifters and attenuators will be included; • Power dividers in Alumina Substrate (Al2O3) / BFN.

The antenna dimensions without the active and beam forming sections are:

• Antenna diameter: 400 mm; • Antenna height: 100 mm, BFN and Active Section not

included.

Accordingly, two possible antenna configurations are envisaged: 1. Config. 1: One single input port, with a single

steerable beam, activated via a dedicated Telemetry and Telecommand section;

2. Config. 2: N input ports, with N possible steerable beams sequentially activated by means of a dedicated switching matrix.

The intercepted area due to the crossing beam is required to evaluate the captured traffic aircraft capability vs. the receiver front end channel saturation. Two possible scenarios have been provided, respectively with 4 and 8 possible beams.

For both configurations (Fig. 13 and 14), followingcomments are applicable:

• In order to highlight the performance in steering of the proposed phased array, the crossing patterns area at -3, -6 and at -9 dB are shown.

• The reds circles in the graphs represent the off nadir conical coverage limits, respectively at 13° and 63°.

• The legends below describe the peak levels and the contour levels.

Figure 13. Phased array, configuration with 8 beams


Figure 14. Phased array, configuration with 4 beams

V. CONCLUSIONS

ADS-B payload feasibility analysis on board the IRIDIUM next generation constellation has been assessed. Potential scenarios have been simulated and accordingly a preliminary payload design has been proposed. The overall resulting performance shows promising solutions that could be further investigated and refined both at mission and at payload level.

REFERENCES[1] RTCA DO 260B: Minimum Operational Performance Standards

(MOPS) for 1090MHz Extended Squitter Automatic Dependent Surveillance – Broadcast (ADS-B)).

[2] RTCA DO 242A: Minimum Aviation System Performance Standards (MAPS) for Automatic Dependent Surveillance Broadcast (ADS-B).

[3] Silvia Porfili, Thales Alenia Space Italia, ADS-B hosted payload feasibility study for IRIDIUM NG Mission Analysis, ref. TASI-BSNAVC-TN-ADS-B-016-10, 28-Jan-2011.

[4] Paolo Noschese. Thales Alenia Space Italia, Host payload for the Iridium S/C Constellation, ADS-B Antenna Technical Note, ref. TASI-BSNAVC-PRP-ADS-B-001-11, 17-Jan-2011.

[5] “TN on Traffic Analysis”, ANTARES ESA Project, ANTAR-B1-OS-TNO-0033-TAI


Independent Surveillance Broadcast – ADS-BReceivers with DOA Estimation

C. Reck #1, M. S. Reuther #, A. Jasch ∗, and L.-P. Schmidt #

#Chair for High Frequency Technology, University of Erlangen–NurembergCauerstraße 9, 91058 Erlangen, Germany

[email protected]∗Institute of Flight Guidance, TU Braunschweig

Hermann-Blenk-Str. 27, 38108 Braunschweig, Germany

Abstract—On the one hand air traffic control (ATC) insti-tutions consider to use ADS-B for its low cost and simplicityof ground station hardware. Aircraft positions transmitted viaADS-B are on the other hand in some cases error prone. In theproposed approach, direction of arrival (DOA) estimation is usedto verify ADS-B airborne positions. The potential positioningerror of ADS-B is thereby evaluated by comparing DOA estimatesto DOA calculated from ADS-B references. To proof the accuracyof the applied DOA estimation sensor, an additional measurementcampaign using a dedicated measurement aircraft has beenconducted.

I. INTRODUCTION

The Automatic Dependent Surveillance Broadcast (ADS-B) is an amelioration to the Secondary Surveillance Radar(SSR) in Mode S. Unlike the regular SSR system, whichbroadcasts radio telegrams mainly on prior request by groundstations, ADS-B uses spontaneous transponder broadcasts onthe Aloha-Protocol. Offering more information than just al-titude and identification, ADS-B also transmits the carryingaircraft’s position as it is gathered by its onboard navigationsystem.Additionally, ground speed, heading and many other infor-mation is provided. As the number of aircraft equipped withADS-B is rising (currently 65% of Mode S equipped aircraft[1]), the system becomes increasingly attractive to feed airtraffic control displays.According to field studies [2], the most part of ADS-Btransponders are broadcasting reliable positioning information,where positions’ root mean squared error (RMSE) is followinga Rayleigh distribution with a mean value of around 250 m.This would on the one hand mean an acceptable error formonitoring en route traffic. On the other hand, there are sometransponders that produce much larger errors. One possibleproblem leading to those large errors is improper wiring ofthe ADS-B transponder with the onboard navigation system.This is one of the reasons why ADS-B is not consideredreliable enough for ATC. The situation changes if the ADS-Bground station has the capability to cross-check the transmittedposition with an independentmeasurement. One possibility forthis would be a time difference of arrival based approach. Thedrawback of this method is the need for a whole system ofgeographically distributed ADS-B receivers.The proposed verification method by a single direction of

Fig. 1. Verification of ADS-B position by a DOA sensor

arrival (DOA) estimation sensor like shown in Fig. 1 is moreattractive in this case skipping the need for linking with otherground stations.In our previous work [3] [4], subspace based DOA estima-

tion by multichannel receivers showed reliable estimates. Inthis contribution, the possible interaction of such a DOA sen-sor with an included ADS-B receiver is discussed. Thereforeresults gathered by field tests using ADS-B as well as by adedicated measurement aircraft are compared.

II. DIRECTION OF ARRIVAL ESTIMATION

The principle of DOA estimation can be visualized byassuming a plane wave (farfield assumption) impinging on anarray of antennas like shown in Fig. 2. We restrict the antennaarray to a Uniform Linear Array (ULA) with half wavelengthspacing to avoid ambiguities. The signals received from thefour antennas show a characteristic phase offset betweenneighboring channels according to the impinging angle.Ideally, this phase offset is equal between all neighboringchannels. If the receiving channels provide complex signaldetection by application of IQ-mixers, the resulting phaseproperties in between the channels can be used to estimatethe DOA of the impinging signal. The receivers includeA/D-conversion to allow digital signal processing and theapplication of subspace based DOA estimation.The performance of multiple subspace based DOA estimationapproaches in SSR scenarios has been investigated in ourprevious work [4]. By application of calibration methodsbased on Eigenstructure analysis [5] the performances of all


Fig. 2. Reception of a plain wave by a Uniform Linear Array (ULA) ofantennas

Fig. 3. DOA estimation sensor with planar antenna array

ESPRIT and MUSIC variants become very similar. It thusis sufficient to evaluate the results gathered by NC UnitaryESPRIT [6].

III. ANTENNA ARRAY AND RECEIVER

The antenna array used for the field tests is a half–wavelength spaced 8 element planar ULA (see Fig. 3). Twoantennas on each side are terminated by 50 Ω to homogenizethe single antenna characteristics. Those elements are neededeven in case of Eigenstructure based calibration because offringe effects due to the finite array length.Each of the four remaining elements is connected to a de-dicated analog receiver frontend. The resulting IF signals arefed to a four channel, 50 MHz, 8–Bit digitizer card. Signalprocessing is carried out in the digital domain on a PC.The whole system is water-cooled, integrated in a weatherresistant portable aluminium box and can be convenientlycontrolled by wireless LAN.

IV. MEASUREMENTS

A. Scenario

In the following, twomeasurement campaigns are evaluated.One was conducted on the roof of a six story university build-ing, using ADS-B telegrams to compute a DOA reference.A total of 26000 ADS-B airborne position telegrams wererecorded within 8 hours. Airborne positions decoded fromthe ADS-B replies are plotted in Fig. 4(a), showing the angleand distance distribution of the positions relative to the DOAsensor. Due to the 8-Bit A/D-converter the dynamic of thereceiver is limited to around 48 dB. Still transponders at a

2020

40

40

60

60

80

80

100

100

120

120

140

140

160

160

180

180

200 km

200 km0◦

180◦

45◦

90◦

135◦Nuremberg

LHFT RadarIngolstadt

Augsburg

Munich

Regensburg

Pilsen

← Latitude

Longitude

→

Reference pos.Sensor pos.Sensor normal

47.54848.54949.55050.5518

9

10

11

12

13

14

(a) ADS-B reference

10

10

20

20

30

30

40

40

50

50 km

45◦

90◦

135◦

Radar

Eltersh.

Wurzburg

← Latitude

Longitude

→

Reference pos.Sensor pos.Sensor norm.

4949.149.249.349.449.549.649.749.88.8

9

9.2

9.4

9.6

9.8

10

10.2

10.4

10.6

10.8

(b) Cessna F172 track

Fig. 4. Reference positions of the two measurement campaigns

distance of up to 200 km were received.A small selected part of 33 telegrams (Airborne Position CodeTYPE ≤ 13) is singled out and applied to calibrate theDOA sensor in the angular range from 45◦ to 135◦ basedon Eigenstructure analysis.The second measurement campaign was carried out at AdolfWurth Airport, Schwabisch Hall, Germany. To evaluate theaccuracy of the constructed DOA sensor, a dedicated referenceaircraft (see Fig. 5) is used. This measurement aircraft isowned and operated by the Institute of Flight Guidance, TUBraunschweig. It is equipped with a Novatel OEM IV receiverand an iMAR iVRU-FC Inertial Measurement Unit (IMU).In the first step of post processing the recorded GPS data isimproved by differential corrections. In a following step theGPS data and the data provided by the inertial measurementunit are merged, taking the lever arms between GPS antenna


Fig. 5. Measurement aircraft (TU Braunschweig): Cessna F172 Skyhawk

TYPE →

RMSE

[◦]→

10 12 14 16 1810−1

100

101

Fig. 6. DOA estimation RMSE versus TYPE-number

and IMU into account. The resulting reference track providesan accuracy in the sub-meter range.The gathered reference positions shown in Fig. 4(b) are notideally distributed over the angular range and the covereddistance is smaller than with the ADS-B campaign. The flighttrack consists mainly of several aerodrome circlings around theairport and then part of the aircraft track back to Braunschweiguntil connection is lost. The comparably small range is due tothe low transponder power (70 W).Nevertheless, 1170 Mode S telegrams were recorded andrelated to the aircraft position based on its time stamps. Again33 telegrams were used for Eigenstructure calibration in theangular range from 45◦ to 135◦.

B. Results

The database of recorded ADS-B airborne position tele-grams were transmitted by a large mixture of differenttransponders. Not all of those transponders provide reliablepositioning information. The standard for ADS-B [7] definesa number of reliability classes encoded in the airborne positionTYPE code. With each of those classes a dedicated contain-ment radius around the transmitted position is defined in whichthe real position needs to be located with a percentage of 95%.The database of recorded ADS-B telegrams containsTYPE ∈ [10, 11, 12, 13, 14, 17, 18]. The database is nowsorted for TYPE-numbers and the RMSE of the differencesbetween reference angles and DOA estimates is plotted versusthe according TYPE in Fig. 6. The RMSE remains aboutequal and below 1◦ with all TYPE-numbers save TYPE ∈[17, 18], where the RMSE ascends to more than 3◦. As acorrelation of low accuracy class and DOA RMSE is evident,those telegrams should not be trusted.

Distance [km] →

RMSE

[◦]→

All TYPE

Only TYPE ≤ 13

50 100 15010−1

100

101

Fig. 7. DOA estimation RMSE versus distance with and without TYPE-number filtering

DOA [◦] →Error

[◦]→

|Error|RMSE

10 45 90 135 170

10−1

100

101

(a) Error vs. DOA

Error [◦] →

Num

berofestim

ates

→

-10 -5 0 5 100

100

200

300

400

500

600

(b) Histogram

Fig. 8. DOA difference of ADS-B reference and DOA estimation sensor

As DOA error according to horizontal positioning error de-creases with increasing distance, the resulting RMSE shoulddecrease over distance too. Thus, the database is sorted fordistance by bins of 30 km and the resulting RMSE of eachbin is plotted versus the according distance in Fig. 7. As an-ticipated a clear decrease of RMSE is observedwith increasingdistance when the whole database is regarded. If all telegramswith TYPE > 13 (a total of 16000 telegrams remain) areexcluded, the decrease in RMSE with increasing distance ismuch lower. At a distance of 135 km both curves meet, at anRMSE of 0.63◦.Using this selected database with TYPE > 13, an errorplot versus impinging angle can be computed like shown inFig. 8(a). By restricting the angular range by 45◦ to 135◦, thehistogram shown in Fig. 8(b) visualizes the according errordistribution. The RMSE in this angular range computes to0.9◦. The results gathered by using the measurement aircraftare shown in Fig. 9. Regarding the RMSE values versus DOAa slightly smaller error is observed. Computing the RMSEfrom 45◦ to 135◦ produces a value of 0.66◦ by evaluating616 reference positions in this range.

V. CONCLUSION

To show the potential of ADS-B receivers with DOA esti-mation function, twomeasurement campaigns were conducted.By evaluating a large number of ADS-B airborne positionsand comparing the resulting reference DOAs with estimatedDOAs, a clear correlation between accuracy class and DOAerror was found. Depending on the aircraft distance, a crosscheck between DOA estimate and ADS-B position can thus


DOA [◦] →

Error

[◦]→

|Error|RMSE

10 45 90 135 170

10−1

100

101

(a) Error vs. DOA

Error [◦]

Num

berofestim

ates

→

-10 -5 0 5 100

10

20

30

40

50

60

(b) Histogram

Fig. 9. DOA difference of differential GPS reference and DOA estimationsensor

gather valuable information regarding ADS-B reliability.By a dedicated reference aircraft the potential of the con-structed DOA sensor to resolve DOA down to a RMSE of0.66◦ has been proven.

ACKNOWLEDGMENT

The authors would like to thank A. Pawlitzki of Thales ATMand the ATC crew of Airport Schwabisch Hall for supportingour measurement campaign. This work has been funded byiAd GmbH and Federal Ministry of Education and Research,Germany.

REFERENCES

[1] C. Rekkas and M. Rees, “Towards ads–b implementation in europe,” inESAV’08, 2008.

[2] V. Cedrini, C. Zacchei, and V. Zampognaro, “Ads-b 1090es implementa-tion: the cristal-med project,” in ESAV’08, 2008.

[3] C. Reck, U. Berold, and L.-P. Schmidt, “High Precision DOA Estimationof SSR Transponder Signals,” in IEEE International Conference onWireless Technology and Systems, 2010.

[4] ——, “Robust doa estimation of ssr signals for aircraft positioning,” inIEEE Radio Wireless Week, 2011.

[5] C. Reck, U. Berold, J. Schuer, and L.-P. Schmidt, “Direction of ArrivalSensor Calibration based on ADS–B Airborne Position Telegrams,” inEuropean Radar Conference, 2009.

[6] M. Haardt and F. Roemer, “Enhancements of Unitary Esprit for Non-Circular Sources,” in IEEE International Conference on Acoustics, Speechand Signal Processing, 2004.

[7] DO-260A, “Minimum Operational Performance Standards for 1090 MHzExtended Squitter Automatic Dependent Surveillance - Broadcast (ADS-B) and Traffic Information Services - Broadcast (TIS-B),” RTCA, Inc.,Tech. Rep., 2003.




LIST OF AUTHORS

A

Abbud JorgeAmeduri SalvatoreAndersson ViktorAndersson BörjeAnniballi EnricoArbuckle Paul Douglas

Universidad Politécnica de Madrid, SpainCIRA, ItalySAAB Electronic Defence Systems, SwedenSwedish Defence Research Agency, SwedenSESM s.c.a.r.l., ItalyUS Federal Aviation Administration, USA

173111

89, 10189, 101

9541

B

Balbastre Tejedor V. JuanBarbaresco FrédéricBarsheshat AbrahamBaud OlivierBenvenuti DarioBerizzi FabrizioBernabò MarcelloBernardos M. AnaBesada A. JuanBonamente MarcoBosser LucBredemeyer Jochen

Universidad Politécnica de Valencia, SpainTHALES Air Systems, FranceSENSIS Corporation, USATHALES, FranceELETTRONICA, ItalyUniversity of Pisa, ItalySELEX Galileo S.p.A., ItalyUniversidad Politécnica de Madrid, SpainGPDS, Universidad Politécnica de Madrid, SpainD'Appolonia S.p.A., ItalyTHALES Systèmes Aéroportés, FranceFCS Flight Calibration Service GmbH, Germany

167, 185, 25927

197265

89, 10773, 129

73247

173, 247111

89, 101141

C

Capria Amerigo CNIT, Italy 129Cardinali Roberta SESM s.c.a.r.l., Italy 95Carrozza Gabriella SESM s.c.a.r.l., Italy 9Casar R. JoséCho AmConti MicheleCuccoli Fabrizio

Universidad Politécnica de Madrid, SpainKorea Aerospace Research Instit., Republic of KoreaUniversity of Pisa, ItalyCNIT, Italy

24725312973


D

Dalle Mese Enzode los Reyes Davó Elíasde Miguel GonzaloDi Girolamo SergioDinoi LiberoDonisi DomenicoDuca Gabriella

University of Pisa, ItalyUniversidad Politécnica de Valencia, SpainUniversidad Politécnica de Madrid, SpainTHALES Alenia Space ItalyELETTRONICA, ItalyD'Appolonia S.p.A., ItalySICTA, Italy

129167, 185, 259

173, 247213231111

2

E

Essen Helmut Fraunhofer – FHR, Germany 61, 65

G

Galati Gaspare Tor Vergata University, Italy 147, 153, 167, 185Garzelli AndreaGómez Pérez J. EmilioGrandin Jean FrançoisGuerriero Marco

University of Siena, ItalyUnivers. Politécnica de Valencia, SpainTHALES Systèmes Aéroportés, FranceELETTRONICA, Italy

73259

89, 101231

H

Haddon DavidHanson JamesHantscher SebastianHeidger RalfHelm StefanieHess MatthiasHonoré NicolasHultman Peter

EASA, GermanyHelios, United KingdomFraunhofer – FHR, GermanyDFS Deutsche Flugsicherung GmbH, GermanyGerman Aerospace Center (DLR), GermanyDeutsche Flugsicherung GmbH (DFS), GermanyTHALES, FranceSAAB Electronic Defence Systems, Sweden

4719165

141, 203271

141, 24126589

J

Jasch AlexanderJohansson AndersJuge Philippe

TU Braunschweig, GermanySwedish Defence Research Agency, SwedenTHALES Air Systems, France

21989, 101

27


K

Kakubari YasuyukiKang YoungshinKlein MathieuKoch WolfgangKoga TadashiKrasnov A. OlegKulpa S. Krzysztof

Electronic Navigation Research Institute, JapanKorea Aerospace Research Institute, Rep. KoreaTHALES Air Systems, FranceFraunhofer – FKIE, GermanyElectronic Navigation Research Institute, JapanDelft University of Technology, The NetherlandsWarsaw University of Technology, Poland

23725327

61, 6523769

119

L

Lang StefanLawrence E. PeterLee IlhyungLeonardi MauroLorenz P. FrankLuedtke G.Lupidi AlbertoLyul Song Taek

Fraunhofer – FHR, GermanyTHALES, FranceAdvanced Inst. of Science and Tech., KoreaTor Vergata University, ItalyFraunhofer – FHR, GermanyFraunhofer – FKIE, GermanyUniversity of Pisa, ItalyHanyang University, Republic of Korea

61265253

147, 153, 167, 185656573

133

M

Malanowski MateuszMantilla Gaviria A. IvanMartorella MarcoMathias AdolfMerino PedroMisiurewicz JacekMiyazaki HiromiMoneuse Jean FrançoisMoscardini ChristianMušicki Darko

Warsaw University of Technology, PolandUniversidad Politécnica de Valencia, SpainUniversity of Pisa, ItalyDeutsche Flugsicherung GmbH (DFS), GermanyINDRA Sistemas S.A., SpainWarsaw University of Technology, PolandElectronic Navigation Research Institute, JapanTHALES Air Systems, FranceUniversity of Pisa, ItalyHanyang University, Republic of Korea

119167, 185, 259

129203, 241

131192372773

133

N

Neufeldt HolgerNihei ShirouNoschese Paolo

THALES Air Systems GmbH, GermanyElectronic Navigation Research Institute, JapanTHALES Alenia Space Italy

207237213


PPanci GianpieroPark BumjinParkinson AdamPawlitzki AlexanderPelant MartinPetri DarioPetrochilos NicolasPiracci Emilio G.Plšek RadekPorfili SilviaPourvoyeur Klaus

ELETTRONICA, ItalyKorea Aerospace Research Institute, Rep. KoreaHelios, United KingdomTHALES Air Systems GmbH, GermanyERA Beyond Radar, Czech RepublicCNIT, ItalyUniversity of Reims FranceTor Vergata University, ItalyERA Beyond Radar, Czech RepublicTHALES Alenia Space ItalyDeutsche Flugsicherung GmbH (DFS), Germany

231253159207179129147147123213203

Q

Quaranta Vincenzo CIRA, Italy 111

RReck ChristophRekkas ChristosReuther S. MaxRicci YvesRuiz Mojica F. Ruy

University of Erlangen Nuremberg, GermanyEUROCONTROL – BelgiumUniversity of Erlangen Nuremberg, GermanyTHALES Air Systems, FranceUniversidad Politécnica de Valencia, Spain

21935

21927

259

SSaini LucaSamanta SoumemSamczy ski PiotrSchikora MarekSchmidt Lorenz P.Schneider Jean YvesSchröder MartinShim SangwookSmolarczyk MaciejSommer RainerSoto AndrésSouami HakimSpinelli SilvioStanley BenStejskal Vojt chStraub Stephen

THALES, ItalyNational Institute of Technology, IndiaWarsaw University of Technology, PolandFraunhofer – FKIE, GermanyUniversity of Erlangen Nuremberg, GermanyTHALES Air Systems, FranceFraunhofer – FHR, GermanyAdvanced Inst. of Science and Tech., KoreaTelecommunications Research Institute, PolandFraunhofer – FHR, GermanyINDRA Sistemas S.A., SpainTHALES Air Systems, FranceTor Vergata University, ItalyHelios, United KingdomERA Beyond Radar, Czech RepublicDeutsche Flugsicherung GmbH (DFS), Germany

26514711965

21927

61, 65253119

6113

153191

123, 1799


TTatarinov N. VictorTatarinov V. SergeyTavernise PierpaoloTengstrand GöranThomas PaulTigrek R. FiratTomasello Filippo

Tomsk Univ. of Control Syst. and Rad. RussianTomsk Univ. of Control Syst. and Rad. RussianTHALES Alenia Space, ItalySAAB Electronic Defence Systems, SwedenBristow Helicopters, United KingdomDelft University of Technology, The NetherlandsEASA, Italy

79, 8379, 83

1989, 101

536947

UUeda Eisuke Electronic Navigation Research Institute, Japan 237

VVaccaro Claudio SICTA, Italy 2Valle Jorgevan Genderen PietVassilev BorisVassileva BorianaVertua CarloVojá ek Martin

INDRA Sistemas S.A., SpainDelft University of Technology, The NetherlandsTechnical University of Sofia, BulgariaInst. for Information and Com.Tech, BulgariaTHALES, ItalyERA Beyond Radar, Czech Republic

1369, 79, 83

225225265123

WWang ZongboWarok PaulWild Klaus

Delft University of Technology, The NetherlandsFraunhofer – FHR, GermanyFraunhofer – FKIE, Germany

6961, 6561, 65

Y

Yoo Changsun Korea Aerospace Research Institute, Rep. Korea 253

ZZimmermann Ruediger Fraunhofer – FHR, Germany 61, 65


proceedings esav'11

Documents

workshop proceedings

downloaded materials

copyright holder

worldwide copyright

prior written permission

identification systems

noncommercial use

distributed i