1 DISTRIBUTION STATEMENT A – Unclassified, Unlimited Distribution 15 February 2013

Integrity Service Excellence

Tristan Nguyen Program Officer (Acting)

AFOSR/RTC Air Force Research Laboratory

Sensing, Surveillance, and Navigation

Date: 06 03 2013

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2013 AFOSR SPRING REVIEW

NAME: Tristan Nguyen BRIEF DESCRIPTION OF PORTFOLIO: Funding basic research to support integrated RF sensing and surveillance or navigation in possibly GPS-denied environments. Common Challenges: cluttered scene; unknown moving targets; unknown terrains; unpredictable environments; multiple (changing) objectives. LIST SUB-AREAS IN PORTFOLIO:

Waveform Design & Fully Adaptive Radar

Radar Imaging

Target Identification through Imaging

Non GPS-based Navigation

To achieve robust, mobile sensing & communications

To enable unimpeded surveillance

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2013 AFOSR SPRING REVIEW

Connection with other AFOSR Programs:

Sensing, Surveillance, Navigation Information Fusion Complex Networks

Signaling for RF communications

Networked communications modeling and more

Imagery data; close to sensors (right before or after)

Diverse data sources; away from sensors

Network data

Technical Tools: Synthesis of ideas in Physics – EM scatterings; ionospheric scintillation Electrical engineering – Coding; signal analysis or synthesis Mathematics – Fourier integral operators; geometry of wave fronts Statistics – Estimation-Detection theory; information theory

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Integration of Waveform Design and Fully Adaptive Radar

Conceptual Design

Two-block optimization paradigm between waveform generation (Transmit Resource) and channel assessment (Receiver).

New design of diverse transmit sequences, modulation, filtering and decision rules.

New formulation of Mutual Information to significantly improve target detection under cluttered conditions.

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New RF Imaging Modalities

Evolution of SAR Imaging • Mono-static to Multi-static SAR • (Fully) Passive SAR* • Inverse SAR (ISAR) • Interferometric SAR (IfSAR or InSAR)* • Polarimetric SAR (PolSAR) • Ultra-narrowband SAR* • 3D SAR*

Imaging with Sparse Distributed Apertures

Tomographic Imaging with Active Noise * Motivated by Fourier Integrator Operators

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Target Identification with Invariants

Some Research Questions: Finding weaker notions of invariance that live on the image space Characterizing the image space and finding its properties

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Non GPS-based Tracking & Navigation

Integrating Adaptive Sensing with Inference for Navigation and Surveillance Multiple RF sensing modalities are desired to support

navigation RF imaging is key in guidance and navigation Inference of obstructions and opportunistic targets is required Cooperative sensing is required for tracking.

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Retain: Multi-Antenna “Diverse Transmit” and Distributed Sensing Geometric Invariant Analysis of Images Mathematical Innovation in Passive and Opportunistic Radar Surveillance through Coupling of Viewing and Navigating Functions

De-emphasize: Classical Pattern-matching in Target Identification Satellite Resource Optimization Dual-frequency Spatial Light Modulator Adaptive Optics

Program’s Future Direction

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Other DoD Programs

ARO emphasizes Opportunistic Sensing but without RF imaging. PM: Liyi Dai

ONR emphasizes: image understanding (EO); acoustics-based communications, imaging, and target recognition in the underwater environment.

PMs: Behzad Kamgar-Parsi; John Tague

DARPA: Mathematics of Sensing, Exploitation, and Execution (MSEE) emphasizes

video, imagery data and not imaging techniques. PM: Anthony Falcone Mind’s Eyes emphasizes visual intelligence by combining machine vision and

reasoning. PM: James Donlon Information in a Photon (InPho) emphasizes quantum optical imaging against

stealth targets, PM: Mari Maeda

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Collaborations & Transitions

AFRL/RY with academic PIs – M. Rangaswamy with R. Narayanan (Penn State U.) and S. Kay (U. Rhode Island) Joint publications Recruitment of a former summer intern at AFRL who will

be employed after completion of Ph.D. at UC Irvine.

International Efforts:

DRDC Ottawa (Noise Radar)

Singapore Nanyang Technical University (INS and Integrated Geo-location/Timekeeping)

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Highlights of Projects

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Bistatic SAR Imaging Using Ultra-Narrowband B. Yazici et al., Rensselaer Polytechnic Institute

Problem: New imaging modality using high Doppler resolution. How: Construct a forward model for imaging by correlating received signal with scaled transmitted signal (Fourier Integral Operator = FIO). Compute iso-Doppler surface. Reconstruct scene reflectivity from iso-Doppler contours via “inverting” the FIO. New Capabilities: Arbitrary ground topography & flight trajectories Passive imaging works as well Preserve edges in images Image is analytically and efficiently constructed.

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Signal Processing via Random Matrix Theory R. Nadakuditi et al., University of Michigan – Anne Arbor

Problem: Formulate random-matrix theoretic approaches to sensor fusion in the low SNR regime How: Define eigen-statistics on random matrix. Study subspaces corresponding to subsets of eigen-spectrum of random matrix.

Preliminary Findings: Dimension reduction should be guided by techniques from random matrix theory. Adding more sensors can degrade the detection performance when SNR is low.

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Cognitive Radar Network for Multiple-Target Tracking A. Nehorai et al., Washington University – St. Louis

Problem: Tracking multiple targets in urban environment with cognitive radars. How to choose the best subset of radars and power allocated to them at each time? Challenges: multipath; high-dimensional state vectors (channel state and targets’ kinematic states) How: Construct a Rao-Blackwell particle filter (to approximate sequential Bayesian filter) for tracking Use the posterior Cramer-Rao bound to schedule radars and optimize power allocation.

Simulation

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Tree-based Context Model for Object Recognition A. Willsky et al., MIT

This work complements the PI’s theoretical research in Gaussian Graphical Models.

Problem: Detect and localize multiple objects in natural images. Recognition of multiple objects in images is very difficult. How: Construct a prior tree-based model that encodes co-occurrence statistics and spatial relationships between objects. Integrate global image features and local detector’s outputs Learn the tree structure from an image database.

Result: Significant improvement over the baseline local detector.

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Estimation of Ionospheric Scintillation Effects J. Morton et al., Miami University

Problem: Use signal processing techniques to detect the plasma drift velocity using GPS carrier phase measurements.

Challenges: multipath; clock errors; dynamics

How: Remove unwanted these effects using a DWT. Find an accurate time-frequency method to generate high-resolution power spectrum of the signal received from multiple (3) antennas. Determine the time differences between scintillation events from the cut-off frequencies on the power spectrum plot. Use antennas’ locations with the time differences to determine the plasma drift velocity.

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Some LRIR Tasks Charles Matson: An information-theoretic investigation of blind deconvolution image processing

Sengveing Amphay: Azimuth-Scanning synthetic Aperture Radar: Signal Processing, Imaging and Data Collection Strategies

Keith Knox: Improved SSA Imaging by the Application of Compressive Sensing Techniques

Daniel Stevens: Characterization of the Method of Time-Frequency Reassignment

David Hughes: Measuring Quantum Data Encrypted Modulation States

Muralidhar Rangaswamy: Waveform Design, Optimization and Adaptive Processing Techniques for multiple input-multiple output (MIMO) Radar and Fully Adaptive Radar (FAR)

Braham, Himed: Radar Waveform Optimization

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Sensing, Surveillance, and Navigation

Conclusion New concepts are needed to closely integrate, at

least, some of the key aspects of sensing, detection, and navigation.

New technical tools in physics, engineering,

mathematics, and statistics are also needed to formalize these concepts.

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Sensing, Surveillance, and Navigation