BRIEF CONTENTS

PREFACE xxxiii

CONTRIBUTORS xxxv

PART I FUNDAMENTALS OF POSITION LOCATION

CHAPTER 1 WIRELESS POSITIONING SYSTEMS: OPERATION, APPLICATION, AND COMPARISON 3

Seyed A. (Reza) Zekavat, Michigan Tech UniversityStuti Kansal, Michigan Tech UniversityAllen H. Levesque, Worcester Polytechnic Institute

CHAPTER 2 SOURCE LOCALIZATION: ALGORITHMS AND ANALYSIS 25

H. C. So, City University of Hong Kong

CHAPTER 3 SECURITY ISSUES FOR POSITION LOCATION 67

Jeong Heon Lee, Virginia TechR. Michael Buehrer, Virginia Tech

CHAPTER 4 CHANNEL MODELING AND ITS IMPACT ON LOCALIZATION 105

Seyed A. (Reza) Zekavat, Michigan Technological University

CHAPTER 5 COMPUTATIONAL METHODS FOR LOCALIZATION 137

Fardad Askarzadeh, Worcester Polytechnic InstituteYunxing Ye, Worcester Polytechnic InstituteUmair I. Khan, Worcester Polytechnic InstituteFerit Ozan Akgul, Worcester Polytechnic InstituteKaveh Pahlavan, Worcester Polytechnic InstituteSergey N. Makarov, Worcester Polytechnic Institute

v

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PART II TOA- AND DOA-BASED POSITIONING

CHAPTER 6 FUNDAMENTALS OF TIME-OF-ARRIVAL-BASED POSITION LOCATION 175

R. Michael Buehrer, Virginia TechSwaroop Venkatesh, Virginia Tech

CHAPTER 7 A REVIEW ON TOA ESTIMATION TECHNIQUES AND COMPARISON 213

Mohsen Pourkhaatoun, Michigan TechSeyed A. (Reza) Zekavat, Michigan Tech

CHAPTER 8 WIRELESS LOCALIZATION USING ULTRA-WIDEBAND SIGNALS 245

Liuqing Yang, Colorado State UniversityHuilin Xu, QUALCOMM Incorporated

CHAPTER 9 AN INTRODUCTION TO DIRECTION-OF-ARRIVAL ESTIMATION TECHNIQUES VIA ANTENNA ARRAYS 279

Seyed A. (Reza) Zekavat, Michigan Tech

CHAPTER 10 SMART ANTENNAS FOR DIRECTION-OF-ARRIVAL INDOOR POSITIONING APPLICATIONS 319

Stefano Maddio, University of FlorenceAlessandro Cidronali, University of FlorenceGianfranco Manes, University of Florence

PART III RECEIVED SIGNAL STRENGTH-BASED POSITIONING

CHAPTER 11 FUNDAMENTALS OF RECEIVED SIGNAL STRENGTH-BASED POSITION LOCATION 359

Jeong Heon Lee, Virginia TechR. Michael Buehrer, Virginia Tech

CHAPTER 12 ON THE PERFORMANCE OF WIRELESS INDOOR LOCALIZATION USING RECEIVED SIGNAL STRENGTH 395

Jie Yang, Stevens Institute of TechnologyYingying Chen, Stevens Institute of TechnologyRichard P. Martin, Rutgers UniversityWade Trappe, Rutgers UniversityMarco Gruteser, Rutgers University

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CHAPTER 13 IMPACT OF ANCHOR PLACEMENT AND ANCHOR SELECTION ON LOCALIZATION ACCURACY 425

Yingying Chen, Stevens Institute of TechnologyJie Yang, Stevens Institute of TechnologyWade Trappe, Rutgers UniversityRichard P. Martin, Rutgers University

CHAPTER 14 KERNEL METHODS FOR RSS-BASED INDOOR LOCALIZATION 457

Piyush Agrawal, University of UtahNeal Patwari, University of Utah

CHAPTER 15 RF FINGERPRINTING LOCATION TECHNIQUES 487

Rafael Saraiva Campos, Universidade do Estado do Rio de JaneiroLisandro Lovisolo, Universidade do Estado do Rio de Janeiro

PART IV LOS/NLOS LOCALIZATION–IDENTIFICATION–MITIGATION

CHAPTER 16 AN INTRODUCTION TO NLOS IDENTIFICATION AND LOCALIZATION 523

Wenjie Xu, Michigan Technological UniversityZhonghai Wang, Michigan Technological UniversitySeyed A. (Reza) Zekavat, Michigan Technological University

CHAPTER 17 NLOS MITIGATION METHODS FOR GEOLOCATION 557

Joni Polili Lie, Nanyang Technological UniversityChin-Heng Lim, Nanyang Technological UniversityChong-Meng Samson See, DOS National Laboratories

CHAPTER 18 MOBILE POSITION ESTIMATION USING RECEIVED SIGNAL STRENGTH AND TIME OF ARRIVAL IN MIXED LOS/NLOS ENVIRONMENTS 583

Bamrung Tau Sieskul, University of VigoFeng Zheng, Leibniz University HannoverThomas Kaiser, University of Duisburg Essen

PART V MOBILITY AND TRACKING USING THE KALMAN FILTER

CHAPTER 19 IMPLEMENTATION OF KALMAN FILTER FOR LOCALIZATION 629

Ossama Abdelkhalik, Michigan Technological University

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CHAPTER 20 REMOTE SENSING TECHNOLOGIES FOR INDOOR APPLICATIONS 649

Seong-hoon Peter Won, University of WaterlooWilliam Wael Melek, University of WaterlooFarid Golnaraghi, Simon Fraser University

CHAPTER 21 MOBILE TRACKING IN MIXED LINE-OF-SIGHT/NON-LINE-OF-SIGHT CONDITIONS: ALGORITHMS AND THEORETICAL LOWER BOUND 685

Liang Chen, Tampere University of TechnologySimo Ali-Löytty, Tampere University of TechnologyRobert Piché, Tampere University of TechnologyLenan Wu, Southeast University

CHAPTER 22 THE KALMAN FILTER AND ITS APPLICATIONS IN GNSS AND INS 709

Emanuela Falletti, Istituto Superiore Mario BoellaMarco Rao, Università di PalermoSimone Savasta, Politecnico di Torino

PART VI NETWORK LOCALIZATION

CHAPTER 23 COLLABORATIVE POSITION LOCATION 755

R. Michael Buehrer, Virginia TechTao Jia, Virginia Tech

CHAPTER 24 POLYNOMIAL-BASED METHODS FOR LOCALIZATION IN MULTIAGENT SYSTEMS 813

Iman Shames, The Australian National University and National ICT AustraliaBariş Fidan, University of WaterlooBrian D. O. Anderson, The Australian National University and National ICT AustraliaHatem Hmam, Electronic Warfare Radar Division, Defence Science & Technology Organisation

CHAPTER 25 BELIEF PROPAGATION TECHNIQUES FOR COOPERATIVE LOCALIZATION IN WIRELESS SENSOR NETWORKS 837

Vladimir Savic, Polytechnic University of MadridSantiago Zazo, Polytechnic University of Madrid

CHAPTER 26 ERROR CHARACTERISTICS OF AD HOC POSITIONING SYSTEMS 871

Dragoş Niculescu, University Politehnica of BucharestBdri Nath, Rutgers University

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CHAPTER 27 SELF-LOCALIZATION OF UAV FORMATIONS USING BEARING MEASUREMENTS 899

Iman Shames, The Australian National University and National ICT AustraliaBariş Fidan, University of WaterlooBrian D. O. Anderson, The Australian National University and National ICT AustraliaHatem Hmam, Electronic Warfare Radar Division, Defence Science & Technology Organisation

PART VII APPLICATIONS

CHAPTER 28 OVERVIEW OF GNSS SYSTEMS 923

Fabio Dovis, Politecnico di TorinoPaolo Mulassano, Istituto Superiore Mario BoellaFabrizio Dominici, Istituto Superiore Mario Boella

CHAPTER 29 DIGITAL SIGNAL PROCESSING IN GNSS RECEIVERS 975

Maurizio Fantino, Istituto Superiore Mario BoellaLetizia Lo Presti, Politecnico di TorinoMarco Pini, Istituto Superiore Mario Boella

CHAPTER 30 RFID-BASED AUTONOMOUS MOBILE ROBOT NAVIGATION 1023

Sunhong Park, Korea Automotive Technology InstituteGuillermo Enriquez, Waseda UniversityShuji Hashimoto, Waseda University

CHAPTER 31 CELLULAR-BASED POSITIONING FOR NEXT-GENERATION TELECOMMUNICATION SYSTEMS 1055

Po-Hsuan Tseng, National Chiao Tung UniversityKai-Ten Feng, National Chiao Tung University

CHAPTER 32 POSITIONING IN LTE 1081

Ari Kangas, Ericsson ABIana Siomina, Ericsson ABTorbjörn Wigren, Ericsson AB

CHAPTER 33 AUTOMATED WILDLIFE RADIO TRACKING 1129

Robert B. MacCurdy, Cornell UniversityRichard M. Gabrielson, Cornell UniversityKathryn A. Cortopassi, Cornell University

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CHAPTER 34 AN INTRODUCTION TO THE FUNDAMENTALS AND IMPLEMENTATION OF WIRELESS LOCAL POSITIONING SYSTEMS 1169

Seyed A. (Reza) Zekavat, Michigan Tech

INDEX 1195

MATLAB codes for various chapters in this book can be found online at ftp://ftp.wiley.com/public/sci_tech_med/matlab_codes.

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PREFACE xxxiii

CONTRIBUTORS xv

PART I FUNDAMENTALS OF POSITION LOCATION

CHAPTER 1 WIRELESS POSITIONING SYSTEMS: OPERATION, APPLICATION, AND COMPARISON 3

1.1 Introduction 31.2 Basic Methods Used in Positioning Systems 5

1.2.1 TOA Estimation 51.2.2 Time-Difference-of-Arrival (TDOA) Estimation 71.2.3 DOA Estimation 81.2.4 RSSI 81.2.5 LOS versus NLOS 81.2.6 Positioning, Mobility, and Tracking 81.2.7 Network Localization 9

1.3 Overview of Positioning Systems 91.3.1 GPS 9

Distance Measurement 10Satellite Positions 12

1.3.2 Assisted Global Positioning System (AGPS or Assisted GPS) 121.3.3 INS 13

INS Classifi cation 141.3.4 Integrated INS and GPS 141.3.5 RFID 14

RFID as a Positioning System 151.3.6 WLPS 151.3.7 TCAS 171.3.8 WLAN 171.3.9 Vision Positioning System 181.3.10 Radar 18

1.4 Comparison of Basic Methods and Positioning Systems 181.5 Conclusion, Summary, and Future Applications 19 References 21

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CHAPTER 2 SOURCE LOCALIZATION: ALGORITHMS AND ANALYSIS 25

2.1 Introduction 262.2 Measurement Models and Principles for Source Localization 28

2.2.1 TOA 282.2.2 TDOA 302.2.3 RSS 312.2.4 DOA 33

2.3 Algorithms for Source Localization 342.3.1 Nonlinear Approaches 34

NLS 34ML 40

2.3.2 Linear Approaches 44LLS 44WLLS 50Subspace 53

2.4 Performance Analysis for Localization Algorithms 552.4.1 CRLB Computation 562.4.2 Mean and Variance Analysis 58

2.5 Conclusion 63 Acknowledgment 64 References 64 Appendix 66

CHAPTER 3 SECURITY ISSUES FOR POSITION LOCATION 67

3.1 Introduction and Motivation 673.1.1 Why Is Location Security Important? 683.1.2 Defi nition of Position Location Security 693.1.3 Relationship to Network Security 69

3.2 Types of Position Location Attacks 693.2.1 APS 70

Modifi cation of Attack Position 70Disruption of Attack Position 72Recent Work 73

3.2.2 ASS 73Modifi cation of Legitimate Position 74Disruption of Legitimate Position 74Recent Work 74

3.2.3 Location Disclosure 75Recent Work 76

3.3 Impact and Analysis of Location Attacks 763.3.1 Adversary and Simulation Models 773.3.2 Optimality Criterion (Risk Measure) 803.3.3 Estimator Error Behavior under Attack 80

Impact of Location Attacks 81Impact of Incorrect PL Estimation 83

3.3.4 Analysis of the Estimator Error Behavior 843.4 Attack Detection and Localization 86

3.4.1 Exploiting Geometric Features of Location Error 89Residual Error Map and Node Convex Hull (NCH) 89GF 92

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3.4.2 Attack Detection 92Statistical Detection Technique 93Geometric Pattern Matching for Attack Detection 95Performance Evaluation 96

3.4.3 Adversary Localization 98Noncooperative Position Location 98Handling Position Outliers 99Performance Evaluation 99

3.5 Conclusion and Continuing Work 102 References 102

CHAPTER 4 CHANNEL MODELING AND ITS IMPACT ON LOCALIZATION 105

4.1 Introduction 1054.2 Channel Model 1074.3 Important Statistics for Received Signal Strength (RSS) 1094.4 Important Statistics for TOA, TDOA, and DOA 113

4.4.1 PDP Statistics and Impact on Localization and Radio Design 1144.4.2 PSP Statistics and Impact on Localization and Radio Design 1204.4.3 PAP Statistics and Impact on Localization and Radio Design 124

4.5 Summary of Different Channel Categories 1254.6 Statistics of Amplitude, Phase, and TOA 126

4.6.1 Fade Amplitude 1264.6.2 Fade-Phase Statistics 1274.6.3 TOA 128

4.7 Other Channel Models 1294.7.1 Geometric-Based Single-Bounce Statistical Channel Modeling 1294.7.2 Circular and Elliptical Geometric Models 1294.7.3 Rough Surface Channel Modeling 1304.7.4 Near-Ground Channel Modeling 1304.7.5 Foliage Effects 132

4.8 Conclusions 133 Acknowledgments 133 References 133

CHAPTER 5 COMPUTATIONAL METHODS FOR LOCALIZATION 137

5.1 Importance of Channel Modeling 1375.2 Important Channel Model Parameters for Localization 1405.3 TOA-Based Techniques 142

5.3.1 Challenges for TOA Techniques 1425.3.2 Simulation and Measurement Techniques 1445.3.3 Channel Measurement Technology 1465.3.4 RT Algorithm 1475.3.5 FDTD Method 148

5.4 Computational Method and the Effect of Micrometals 1515.4.1 FDTD and the Effects of Micrometals 1515.4.2 2-D FDTD Simulation Scenarios 1535.4.3 Comparison of Computation with Empirical Results 1565.4.4 Ray Optics and Effects of Micrometals 157

Analysis of Diffraction around the Edges 159Comparison of Computation with Empirical Results 160

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5.5 FDTD and the Effects of the Human Body 1635.5.1 Measurement of Wideband Characteristics 1645.5.2 Computational Analysis of the Effects of the Human Body 166

An Overview of Ansoft HFSS 167Analysis of Path Loss Models 167Experimental Procedure Using the Ansoft HFSS Suite 168

5.6 Conclusion 170 Acknowledgments 170 References 171 Appendix 172

PART II TOA- AND DOA-BASED POSITIONING

CHAPTER 6 FUNDAMENTALS OF TIME-OF-ARRIVAL-BASED POSITION LOCATION 175

6.1 Introduction 1756.2 TDOA Positioning 176

6.2.1 Geometric Interpretation 1776.2.2 Uplink versus Downlink Measurements 180

6.3 TOA Positioning 1806.3.1 Geometric Interpretation 181

6.4 TDOA versus TOA 1836.5 TOA versus TDOA in the Presence of Noise 1846.6 Linearization 187

6.6.1 Taylor Series Approximation 1876.6.2 Differencing 1896.6.3 Linearization of TDOA 196

6.7 Pseudorange 1966.8 The Impact of NLOS Propagation 199

6.8.1 Impact of NLOS Bias Errors 1996.8.2 Discarding NLOS Range Estimates 2006.8.3 NLOS Identifi cation 2026.8.4 NLOS Mitigation 205

6.9 Handling NLOS Errors: a Linear Programming Approach 2066.9.1 LOS Range Estimates 2066.9.2 NLOS Range Estimates 2076.9.3 Combining the LOS and NLOS Range Information 208

6.10 Conclusions 211 References 211

CHAPTER 7 A REVIEW ON TOA ESTIMATION TECHNIQUES AND COMPARISON 213

7.1 Introduction 2137.2 TOA Estimation Methods 216

7.2.1 Conventional Correlation-Based Techniques 220Pros and Cons 221

7.2.2 Deconvolution Methods 222Pros and Cons 224

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7.2.3 ML-Based Methods 225Pros and Cons 226

7.2.4 Subspace-Based Techniques 226Pros and Cons 228

7.2.5 BSS-Based Algorithms 229Pros and Cons 233

7.3 Comparison of TOA Estimation Techniques 2337.4 Range Estimation System Design 235

7.4.1 Single-Band Range Estimation Architecture 2357.4.2 Multiband Range Estimation: General Architecture 2367.4.3 Noncontiguous Multiband Scenario 238

7.5 Conclusion 240 References 240

CHAPTER 8 WIRELESS LOCALIZATION USING ULTRA-WIDEBAND SIGNALS 245

8.1 Introduction to UWB 2458.1.1 Regularization 2458.1.2 Transmission Approaches 2468.1.3 Standards 2478.1.4 UWB Channels 248

8.2 UWB Localization Techniques 2508.2.1 Fingerprinting Localization 2508.2.2 Geometric Localization 252

TOA Estimation 253Position Estimation 253

8.2.3 NLOS Issues 2548.3 TOA Estimation for IR UWB 255

8.3.1 System Model 2558.3.2 ML TOA Estimation 2578.3.3 Energy Detection-Based TOA Estimation 2588.3.4 TDT 2608.3.5 Discussions on IR-Based TOA Estimation 262

8.4 TOA Estimation for MB-OFDM UWB 2638.4.1 System Model 2658.4.2 Correlation-Based TOA Estimator 2668.4.3 Energy Detection-Based TOA Estimator 2678.4.4 TOA Estimation by Suppressing Energy Leakage 2698.4.5 Discussions on MB-OFDM-Based TOA Estimation 273

8.5 Conclusions 274 References 275

CHAPTER 9 AN INTRODUCTION TO DIRECTION-OF-ARRIVAL ESTIMATION TECHNIQUES VIA ANTENNA ARRAYS 279

9.1 Introduction 2799.2 Antennas and Their Parameters 280

9.2.1 Antenna HPBW 2829.2.2 First Side Lobe to the Main Lobe Power Ratio 2839.2.3 Non-Main Lobe Power (All Side Lobe Power) to Main Lobe

Power Ratio 283

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9.2.4 Antenna Impedance 2839.2.5 Antenna Return Loss 2849.2.6 Antenna Bandwidth 2859.2.7 Antenna Gain 285

Antenna Gain Is Usually Measured in dBi 2869.2.8 Antenna Polarization 287

9.3 Antenna Arrays 2879.3.1 Smart Antennas 2889.3.2 Important Parameters of Antenna Arrays 289

Array Vector 289Array Factor 290Mutual Coupling 291

9.4 DOA Estimation Methods 2939.4.1 DAS 2979.4.2 MUSIC and Root MUSIC 299

MUSIC 299Root MUSIC 301Complexity Analysis 302Comparison of MUSIC and Root MUSIC 304

9.4.3 DAS and Root MUSIC Fusion 306Simulations and Performance Analysis 308

9.4.4 Comparison 3099.5 DOA Estimation for Periodic Sense Transmission 3109.6 Conclusion 315 Acknowledgments 315 References 316

CHAPTER 10 SMART ANTENNAS FOR DIRECTION-OF-ARRIVAL INDOOR POSITIONING APPLICATIONS 319

10.1 Introduction 31910.2 Principles of Indoor Positioning Based on SA 321

10.2.1 Positioning Estimation Techniques 32110.2.2 DOA Principle of Operations 323

10.3 Antenna Technology and Design Principles 32610.3.1 Radiation Pattern 32610.3.2 Circular Polarization 32810.3.3 Antenna Selector 32810.3.4 Signal Detection Circuit 329

10.4 DOA Estimation Accuracy for SAs 33010.4.1 Information Theory Elements 33010.4.2 Derivation of the CRB for 1-D Case Using SAs 331

Effect of Number of Antenna Elements Nr 335Effect of the Directivity Coeffi cient m 335Effect of the RSSI Variance σ 2RSSI 336

10.4.3 Derivation of the CRB for 2-D DOA Using SAs 33710.5 Algorithm for Indoor DOA Estimations 340

10.5.1 1-D DOA Estimation Methods 340Strongest RSSI–Sector Partition 341LSE 341The MUSIC Estimator 343

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10.5.2 2-D DOA Estimation Methods 34410.5.3 2-D DOA Simulated Experiments 345

10.6 Prototype of SA Suitable for Indoor DOA Positioning Applications 34610.6.1 Six Switching Beams Antenna Prototype: Characteristics

and Performance 34610.6.2 Prototype DOA Estimation Performance 349

Strongest RSSI 350Fingerprinting 351MUSIC 352

10.6.3 Experimental Results and Conclusions 35210.7 Discussion and Conclusions 353 References 354

PART III RECEIVED SIGNAL STRENGTH-BASED POSITIONING

CHAPTER 11 FUNDAMENTALS OF RECEIVED SIGNAL STRENGTH-BASED POSITION LOCATION 359

11.1 Introduction and Motivation 35911.1.1 Why Is RSS Attractive for Localization? 36011.1.2 Problem Statement and Outline 360

11.2 Sources of Location Error and Mitigation 36211.2.1 Multipath Fading and NLOS Propagation 36211.2.2 Shadow Fading 36311.2.3 Systematic Bias or Error 36311.2.4 Geometric Node Confi guration 363

11.3 Techniques Using RSS for Position Location 36311.3.1 Range-Based Positioning 364

Statistical Model for RSS 364Basics of Differential RSS 365Spatial Correlation of Shadow Fading 367

11.3.2 RF Fingerprinting 36811.3.3 Proximity-Based Positioning 370

Dimensionality Reduction Using Geographical Proximity 37011.4 Geometric Interpretations of RSS/DRSS Positioning 372

11.4.1 RSS-Based Lateration 37511.4.2 DRSS-Based Lateration 377

Geometry of Relative DRSS Positioning 377Geometry of Absolute DRSS Positioning 379Geometric Solution of DRSS Location 380

11.5 Location Estimators 38011.5.1 Theoretical Limits for Location Estimation 381

Optimality Criterion 381Cramer–Rao Lower Bound (CRLB) 381

11.5.2 ML Estimator 38211.5.3 Nonlinear LS Estimator 383

LS Optimization Framework 38311.5.4 Linear LS Estimator 385

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11.6 Performance Evaluation 38711.6.1 Simulation Settings 387

Numerical Optimization Algorithm Considered 38811.6.2 Simulation Results 388

Impact of Number of Anchor Nodes and Spatial Correlation 388Impact of Correlated Shadow Fading 389Impact of PL and Spatial Correlation 389

11.7 Conclusion 391 References 392

CHAPTER 12 ON THE PERFORMANCE OF WIRELESS INDOOR LOCALIZATION USING RECEIVED SIGNAL STRENGTH 395

12.1 Introduction 39612.2 RSS-based Localization Algorithms 397

12.2.1 Approach Overview 39812.2.2 Lateration Methods 399

NLS 399LLS 400

12.2.3 Classifi cation via Machine Learning 40112.2.4 Probabilistic Approaches 40312.2.5 Statistical Supervised Learning Techniques 40412.2.6 Summary of Localization Algorithms 405

12.3 Localization Performance Study 40712.3.1 Performance Metrics 40712.3.2 Performance Investigation Using Real Wireless Networks 408

Experimental Scenarios 408Performance Results 410

12.4 Enhancing the Robustness of Localization 41312.4.1 Real-Time Infrastructure Calibration 41312.4.2 Effects of Employing Multiple Antennas 41412.4.3 Robust Statistical Methods 41612.4.4 Revisiting Linear Regression 41712.4.5 Exploiting Spatial Correlation 418

12.5 Conclusion and Applications 420 References 422

CHAPTER 13 IMPACT OF ANCHOR PLACEMENT AND ANCHOR SELECTION ON LOCALIZATION ACCURACY 425

13.1 Introduction 42513.2 Anchor Placement 426

13.2.1 Overview 42613.2.2 Impact of Anchor Placement 42813.2.3 Heuristic Search 43113.2.4 Acute Triangular-Based Deployment 43313.2.5 Adaptive Beacon Placement 43513.2.6 Optimal Placement via maxL–minE 436

Theoretical Analysis 436Algorithm Overview and Experimental Evaluation 441

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13.3 Anchor Selection 44513.3.1 Overview 44513.3.2 Joint Clustering Technique 44513.3.3 Entropy-Based Information Gain 44713.3.4 Convex Hull Selection 44713.3.5 Selection from High Density of Anchors 449

13.4 Discussion and Conclusion 453 References 453

CHAPTER 14 KERNEL METHODS FOR RSS-BASED INDOOR LOCALIZATION 457

14.1 Introduction 45714.1.1 Outline of the Chapter 459

14.2 Kernel Methods 45914.2.1 Problem Statement 46014.2.2 General Mathematical Formulation 460

Determination of Kernel Parameters 461Example Framework 462

14.2.3 LANDMARC Algorithm 464Estimation of Parameters 464

14.2.4 Gaussian Kernel Localization Algorithm 465Estimation of Parameters 466

14.2.5 Radial Basis Function-Based Localization Algorithm 468Estimation of Parameters 469

14.2.6 Linear Signal-Distance Map Localization Algorithm 470Estimation of Parameters 472

14.2.7 Summary 47314.3 Numerical Examples 473

14.3.1 MLE 473Estimating Coordinate from RSS 474Implementation Details 474

14.3.2 Description of Comparison Example 47514.4 Evaluation Using Measurement Data Set 481

14.4.1 Measurement Campaign Description 48114.4.2 Evaluation Procedure 48114.4.3 Results 482

Bias Results 482RMSE Results 484

14.5 Discussion and Conclusion 484 References 485

CHAPTER 15 RF FINGERPRINTING LOCATION TECHNIQUES 487

15.1 Introduction 48715.2 RF Fingerprints 48915.3 CDB 490

15.3.1 CDB Structure 490Uniform Grid 491Indexed List 491

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15.3.2 Building the CDB 491Field Measurements 491Propagation Modeling 492Mixing Predicted and Measured Values 498

15.4 Techniques to Reduce the Search Space 49915.4.1 CDB Filtering 500

First Filtering Step 500Second Filtering Step 501Third Filtering Step 501

15.4.2 Optimized Search Using GAs 50215.5 Pattern Matching of RF Fingerprints 504

15.5.1 Distance in N-Dimensional RSS Space 505Particular Case 505Generic Case with Penalty Term 506

15.5.2 Pattern Matching Using ANNs 50815.5.3 Spearman Rank Correlation Coeffi cient 510

15.6 Experimental Performance 51215.6.1 Outdoor 850-MHz GSM Network 51215.6.2 Indoor Wi-Fi Networks 515

15.7 Conclusions 516 References 518

PART IV LOS/NLOS LOCALIZATION–IDENTIFICATION–MITIGATION

CHAPTER 16 AN INTRODUCTION TO NLOS IDENTIFICATION AND LOCALIZATION 523

16.1 Introduction 52416.2 NLOS Identifi cation 525

16.2.1 Cooperative Methods 527DOA Residual Testing 527Time-Difference-of-Arrival (TDOA) Residual 528Residual Distribution Testing 529

16.2.2 Single-Node Methods Based on the Range Statistics 530Techniques Based on Range Measurements Over Time 530Techniques Based on the Range Measurements over Different Frequency Bands 531

16.2.3 Single-Node Methods Based on Channel Characteristics 532Narrow and Wideband Systems 533UWB Systems 534Systems Using Antenna Array 536

16.2.4 Single-Node Hybrid Approach 54116.2.5 Comparison of NLOS Identifi cation Methods 543

16.3 NLOS Localization 54316.3.1 RSSI 54416.3.2 Bidirectional TOA–DOA Fusion 54616.3.3 Single BN TOA–DOA Fusion with the Assistant

Environment Map 547

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16.3.4 Multinode TOA–DOA Fusion 54816.3.5 Comparison 550

16.4 Conclusion 552 References 552

CHAPTER 17 NLOS MITIGATION METHODS FOR GEOLOCATION 557

17.1 Introduction 55817.2 Geolocation System Model 55917.3 A Review of NLOS Mitigation Techniques 560

17.3.1 ML-Based Techniques 560Finding Nh ML Estimates of Unknown Parameters 562Finding the Most Possible Hypothesis 562

17.3.2 LS-Based Techniques 56217.3.3 Constrained Optimization Techniques 56417.3.4 Robust Estimator Techniques 565

17.4 Application of the Single Moving Sensor Geolocation 56617.4.1 Range Measurements Profi le-Based Trimming 56717.4.2 Reconstruction of Trimmed TOA Profi le 57117.4.3 Robust Trimming with Nonparametric Noise Density Estimator 57217.4.4 Performance Analysis 574

17.5 Conclusions 579 References 579

CHAPTER 18 MOBILE POSITION ESTIMATION USING RECEIVED SIGNAL STRENGTH AND TIME OF ARRIVAL IN MIXED LOS/NLOS ENVIRONMENTS 583

18.1 Introduction 58418.1.1 Background 58418.1.2 Literature Review 584

LOS/NLOS Detection 585Wireless Geolocation 586

18.1.3 Merits 58718.1.4 Organization 587

18.2 System Model 58818.2.1 Existing Techniques for Mobile Position Estimation 588

LLS Based on First-Order Taylor Series 589LLS with Additional Parameterization 590AML 591

18.2.2 Path Loss Model 59318.3 Mobile Position Estimation 594

18.3.1 TOA Estimation 594LOS Suffi ciency 595

18.3.2 LS 59618.3.3 WLS 59618.3.4 ML 596

LS Error Variance 59618.4 CRB for Mobile Position Estimation 597

18.4.1 FIM of TOA Estimation 59718.4.2 CRB for TOA Estimation 59818.4.3 CRB for Mobile Position Estimation 598

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18.5 Numerical Examples 59818.6 Conclusions 607 References 608 Appendix 611

PART V MOBILITY AND TRACKING USING THE KALMAN FILTER

CHAPTER 19 IMPLEMENTATION OF KALMAN FILTER FOR LOCALIZATION 629

19.1 Introduction 62919.2 The Estimation Problem 63119.3 Formulation of Localization as an Estimation Problem 63219.4 Discrete Linear Kalman Filter 633

19.4.1 Kalman Filter Derivation 63319.4.2 Discussion and Implementation 635

19.5 Continuous Kalman Filter 64119.6 Extended Kalman Filter 64319.7 Further Reading 646 References 646

CHAPTER 20 REMOTE SENSING TECHNOLOGIES FOR INDOOR APPLICATIONS 649

20.1 Position Sensing Technology 65020.1.1 Vision-Based Position Sensors 65020.1.2 Non-Vision-Based Position Sensor 65320.1.3 Inertial Sensors 657

Orientation Calculation Using Quaternion 657Position Calculation Using Inertial Sensors 660IMU 662

20.1.4 Applications 66520.2 Bayesian Estimators 667

20.2.1 Bayes Filter 66820.2.2 KF 67020.2.3 Extended KF 67120.2.4 PF 67320.2.5 Filter Comparison Example 67520.2.6 Filter Applications 677

20.3 Summary 679 References 680

CHAPTER 21 MOBILE TRACKING IN MIXED LINE-OF-SIGHT/NON-LINE-OF-SIGHT CONDITIONS: ALGORITHMS AND THEORETICAL LOWER BOUND 685

21.1 Introduction 68521.2 System Description 686

21.2.1 General Problem Formulation 68621.2.2 Example of the State Model 68821.2.3 Example of the Measurement Model 688

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21.3 Tracking Algorithm Based on GMF 68921.3.1 The Development of GMF 689

Forgetting Components 692Merging Components 692Convergence Result of GMF 693

21.3.2 The Modifi ed EKF Banks 693Algorithm Description 693

21.4 Tracking Method Based on ARBPF 69521.4.1 Generic PF 69521.4.2 Approximated RBPF 696

21.5 Lower Bound of Performance 69921.6 Numerical Results 702

21.6.1 Performance Comparison with Different Algorithms 70321.6.2 Comparison with Posterior CRLB 70421.6.3 Complexity Comparison 705

21.7 Conclusions 706 References 706

CHAPTER 22 THE KALMAN FILTER AND ITS APPLICATIONS IN GNSS AND INS 709

22.1 Introduction 71022.2 Review of Kalman Filtering and Extended Kalman Filtering

for Navigation 71122.2.1 State-Space Models 71122.2.2 Continuous Time to Discrete-Time Transformation 71422.2.3 Recursive Estimation and Initial Conditions 71622.2.4 Extended KF 718

Linearized and Extended Architectures 72022.3 EKF-Based PVT Computation in a Stand-Alone GNSS Receiver 721

22.3.1 State-Space Model 72222.3.2 Linearization of the Measurement Equation 724

Pseudorange and Pseudorange Rate Prediction 72622.3.3 Error Covariance Matrices 727

22.4 Inertial Navigation Fundamentals 72822.4.1 Structure of an IMU 72922.4.2 The Coriolis Theorem 73022.4.3 Mechanization Equations 730

Computation and Tracking of the Body Attitude: The Direction Cosine Matrix (DCM) 731Computation and Tracking of the Velocity 732Computation and Tracking of the Position 732

22.5 IMU Alignment 73322.5.1 GNSS-INS Hybridization: State-Space Models 735

22.6 General Architecture for the Loose Integration 73522.6.1 Loose Integration: State-Space Model 735

Space Equation 736Velocity Equation 737Attitude Misalignment Equation 738Accelerometers Bias Equation 739Gyroscopes Bias Equation 739

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22.6.2 Loose Integration: State Transition Matrix 74022.6.3 Loose Integration: Measurement Equation 741

22.7 General Architecture for the Tight Integration 74122.7.1 Tight Integration: State-Space Model 742

Clock Misalignment Equation 743Clock Drift Equation 743

22.7.2 Tight Integration: State Transition Matrix 74322.7.3 Tight Integration: Measurement Equation 744

22.8 General Architecture for the Ultra-Tight Integration 74522.8.1 Ultra-Tight Integration: State-Space Model 74622.8.2 Ultra-Tight Integration: State Transition Matrix 74622.8.3 Ultra-Tight Integration: Measurement Equation 746

22.9 Conclusions 747 References 748 Appendix A 749

PART VI NETWORK LOCALIZATION

CHAPTER 23 COLLABORATIVE POSITION LOCATION 755

23.1 Introduction 75523.2 Problem Defi nition 75823.3 Performance Bounds 760

23.3.1 CRLB 76023.3.2 MLE/Weighted LS 763

The Branch-and-Bound (BB)/Reformulation-Linearization Technique (RLT) Algorithm 764Reformulation and Linearization of the MLE 765Partitioning Variables, Relaxation Errors, and Partitioning Strategies 768

23.3.3 Numerical Results 76823.4 An Overview of Suboptimal Algorithms 771

23.4.1 A Taxonomy of Existing Algorithms 774Type of Measurement Data: Distance, Angle of Arrival (AOA), and RSS Fingerprinting 774Where the Computation Is Performed: Centralized or Distributed 774How the Computation Is Performed: Sequential or Concurrent 774How the Problem Is Formulated: Probabilistic or Nonprobabilistic 775

23.5 Specifi c Suboptimal Approaches 77523.5.1 Sequential LS 77623.5.2 Optimization-Based Approaches 77823.5.3 MDS 78023.5.4 Set-Theoretic Approach: Iterative Parallel Projection

Method (IPPM) 783The Modifi ed Parallel Projection Method (MPPM) 783IPPM for Collaborative Position Location 788

23.6 Numerical Comparison of Approaches 79323.6.1 Localization Accuracy 79323.6.2 Computational Complexity 799

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23.7 NLOS Propagation 80023.7.1 Knowledge about the NLOS Propagation 80123.7.2 NLOS Mitigation Example 80123.7.3 Simulation Results 803

23.8 Summary 807 References 808

CHAPTER 24 POLYNOMIAL-BASED METHODS FOR LOCALIZATION IN MULTIAGENT SYSTEMS 813

24.1 Introduction 81324.2 Polynomial Function Optimization 815

24.2.1 Polynomial Continuation (Homotopy) Methods 81624.2.2 SOS and SDP Approaches 817

24.3 Noisy Target Localization 81924.4 Relative Reference Frame Determination 822

24.4.1 Relative Reference Frame Determination with Distance Measurements 823

24.4.2 Relative Reference Frame Determination with Relative Angle Measurements 824

24.4.3 Noisy Relative Reference Frame Determination 82624.4.4 Algorithmic Comparison with Some Existing Methods 829

Comments on the Complexity of SOS Methods 83024.4.5 Colinear Anchors 831

24.5 An Extension of the SOS Approach 83224.6 Conclusions 833 Acknowledgment 833 References 833

CHAPTER 25 BELIEF PROPAGATION TECHNIQUES FOR COOPERATIVE LOCALIZATION IN WIRELESS SENSOR NETWORKS 837

25.1 Introduction to Cooperative Localization in WSNs 83825.1.1 Classifi cation of Cooperative Localization Methods 838

Range-Based versus Range-Free Methods 838Centralized versus Distributed Methods 839Anchor-Based versus Anchor-Free Methods 839Probabilistic versus Deterministic Methods 839

25.1.2 Measurement Techniques 84025.1.3 Motivating Applications 841

25.2 Probabilistic Localization Based on BP 84225.2.1 Introduction to Probabilistic Localization 842

Statistical Framework for Probabilistic Localization 84225.2.2 Belief Propagation 843

Graphical Model 844Description of the Algorithm 847

25.2.3 NBP 848Computing Messages 848Computing Beliefs 849Convergence of NBP 850

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25.2.4 NBBP 850Modifi cations 850Performance Analysis 851

25.3 Generalized BP Methods 85525.3.1 Correctness of BP 85625.3.2 GBP-K 85725.3.3 NGBP-JT 857

Defi nition 857Example Network 858Nonparametric Approximation 860

25.3.4 NBP-ST 861Spanning Tree Formation 861Performance Analysis 864

25.4 Conclusions 867 Acknowledgments 867 References 867

CHAPTER 26 ERROR CHARACTERISTICS OF AD HOC POSITIONING SYSTEMS 871

26.1 Introduction 87126.2 APS Algorithms 873

26.2.1 DV-Hop Propagation Method 87426.2.2 DV-Euclidean and DV-Radial 87626.2.3 DV-Position 877

26.3 Positioning Error Analysis 87826.3.1 Trilateration Review 87826.3.2 CRLB for Trilateration 87926.3.3 DV-Hop Range Error 87926.3.4 CRLB for DV-Hop Positioning 88326.3.5 DV-Position Error 885

26.4 Discussion 88826.5 Related Work 89026.6 Conclusion 891 References 891 Appendices 892

CHAPTER 27 SELF-LOCALIZATION OF FORMATIONS OF AUTONOMOUS AGENTS USING BEARING MEASUREMENTS 899

27.1 Introduction 89927.2 Problem Setup 90127.3 A Rigid Graph Theoretical Framework for Formation Localization 90327.4 Four-Bar Linkage Mechanisms 90627.5 A Localization Algorithm Based on Four-Bar Linkage Mechanisms 90827.6 Localization of Larger Formations 91427.7 Localization with Extra Landmarks 91627.8 Availability of More Angle Measurements for Three Agents 91727.9 Conclusions 918 Acknowledgments 919 References 919

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PART VII APPLICATIONS

CHAPTER 28 OVERVIEW OF GLOBAL NAVIGATION SATELLITE SYSTEMS 923

28.1 Introduction 92328.1.1 What Is Radio Navigation? 92428.1.2 Spherical Systems 924

Two-Way Measurements 925One-Way Measurement 926

28.1.3 Evolution Programs of GNSS Constellations 92628.2 Principles of Satellite Navigation 927

28.2.1 Geometry and Measurement Errors 92928.2.2 Impact of Measurement Errors on User Position 930

28.3 The Impact of Geometry 93228.3.1 GDOP as a Function of Position and Time 934

28.4 Overview on Reference Systems 93928.4.1 Conventional Inertial Reference System 93928.4.2 Conventional Terrestrial Reference System 94028.4.3 Ellipsoidal Coordinates 94128.4.4 The Geoid 94228.4.5 The Global Datum 94228.4.6 East-North-Up (ENU) Reference Frame 943

28.5 Structure of the Signal In Space (SIS) 94328.5.1 GNSS Frequency Plan 94428.5.2 The Binary Offset Carrier (BOC) Modulation 944

BOC Power Spectral Density 946Correlation Properties 946BOC versus BOCcos 948

28.5.3 The GNSS Transmitted Signal 94928.6 Current and Modernized GPS Signals 950

28.6.1 Multiplexed BOC (MBOC) Signal Baseline 95128.6.2 TMBOC Modulation 952

28.7 Galileo System and SIS 95328.7.1 E1 CBOC Modulation 95428.7.2 CASM Multiplexing Scheme 95828.7.3 AltBOC Modulation and Multiplexing Scheme 960

The AltBOC Concept 96128.8 Error Sources for the Position Evaluation 965

28.8.1 GNSS Positioning 966Impact of Ranging Errors on Position Metrics 966

28.9 Augmentations 96828.9.1 Local Area Differential Corrections 96828.9.2 Wide Area Differential Corrections 969

The Integrity Concept 97028.9.3 A-GNSS and Cooperative Navigation 97128.9.4 Trend of GNSS-Related Augmentation Solutions and Technologies 972

28.10 Conclusions 972 Acknowledgment 973 References 973

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CHAPTER 29 DIGITAL SIGNAL PROCESSING IN GNSS RECEIVERS 975

29.1 Received Signal 97629.1.1 The Doppler Effect in the Carrier 97729.1.2 The Doppler Effect at Baseband 978

29.2 The General Receiver Structure 97829.2.1 Sampling Frequency 97929.2.2 The Digital IF Signal 980

Carrier-to-Noise Ratio and Signal-to-Noise Ratio (SNR) 98029.3 Acquisition 985

29.3.1 Detection and Estimation Main Strategy 986Parameter Estimation 986Detection 987

29.3.2 Cross-Ambiguity Function (CAF) 988The SS 989Consideration on the Value of the Frequency Bin Size 990Consideration on the Value of the Delay Bin Size 992SNR at the CAF Peak 992Coherent and Noncoherent Integration 993

29.3.3 Refi nement of the Estimation of the SIS Parameters 99429.4 The Role of FFT in a GNSS Receiver 996

29.4.1 FFT in the Time Domain 99729.4.2 FFT in the Doppler Domain 998

29.5 Estimation of the Propagation Delay 99929.6 Methods for SIS Detection 1000

29.6.1 NP Approach 1000NP Detection in GNSS 1002

29.6.2 Detection Based on the A Posteriori Probabilities 100329.6.3 Bayesian Sequential Detection 1003

Sequential Detection in GNSS 100529.7 Gradient Method for SIS Parameters Estimation 1006

29.7.1 Transient between Signal Acquisition and Tracking 100629.7.2 Fundamentals on the Gradient Theory 100729.7.3 Application to GNSS Signals 1009

29.8 Null Seeker and Tracking Loops 101129.8.1 DLL 1013

Discrimination Function 101429.8.2 Carrier Tracking 101629.8.3 Models of the Tracking Loops 1017

29.9 Conclusions 1018 References 1019 Appendix 1021

CHAPTER 30 AUTONOMOUS MOBILE ROBOT NAVIGATION SYSTEMS USING RFID AND THEIR APPLICATIONS 1023

30.1 Robust RFID-Based Navigation System 102330.1.1 Basic Navigation Concepts 102330.1.2 Estimating Robot’s Pose 102630.1.3 Experimental Verifi cation: Grid-Like Pattern 1028

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30.2 Reduction of Localization Error: Read Time 103130.2.1 Problem with RFID-Based Localization 103130.2.2 Read-Time Concept 103230.2.3 Randomly Distributed RFID Tags 103430.2.4 Experimental Verifi cation: Grid-Like and

Random Pattern 103430.2.5 Path Trajectories with Grid-Like Pattern 103630.2.6 Comparison with Other RFID-Based Methods 1039

30.3 Extending the Read-Time Paradigm 104030.3.1 Static Obstacle Avoidance 104030.3.2 Multiple Obstacles Avoidance 104030.3.3 Experimental Verifi cation: Static Single/Multiple Obstacles 104330.3.4 Navigation with a Single Static Obstacle 104330.3.5 Navigation with Multiple Static Obstacles 1045

30.4 Applications and Extensions 104730.4.1 Application Concepts 104730.4.2 Extension Possibilities 1048

30.5 Conclusions 1053 References 1054

CHAPTER 31 CELLULAR-BASED POSITIONING FOR NEXT-GENERATION TELECOMMUNICATION SYSTEMS 1055

31.1 Introduction 105631.2 An Overview of LBS in Next-Generation Telecommunication Systems 1058

31.2.1 Basic LBS Support: DL Preamble Measurements 1059Basic LBS Support with Interference Cancellation 1063

31.2.2 Enhanced LBS Support: D-LBS Zones 106331.2.3 Basic LBS Support: UL Ranging Measurements 1065

31.3 A Case Study: LBS Performance of the IEEE 802.16m 106931.3.1 Link-Level Simulation: TOA Estimation of the IEEE 802.16m

Standard 106931.3.2 System-Level Simulation: DL LBS Performance of the IEEE 802.16m

Standard 107231.3.3 System-Level Simulation: UL LBS Performance of the IEEE 802.16m

Standard 107631.3.4 Comparison of DL and UL LBS 1077

31.4 Conclusion 1078 Acknowledgments 1079 References 1079

CHAPTER 32 POSITIONING IN LTE 1081

32.1 Introduction 108232.1.1 System Architecture 108232.1.2 Radio Access Network 108232.1.3 Core Network 108332.1.4 Air Interface 1083

DL 1083UL 1085

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32.2 Requirements on Positioning in LTE 108632.2.1 3GPP Requirements 108632.2.2 Emergency Positioning 108632.2.3 Location-Based Services (LBSs) 1087

32.3 Positioning Architecture and Signaling in LTE 108732.3.1 Control Plane 108932.3.2 User Plane 1090

32.4 Positioning Procedures in LTE 109032.4.1 Signaling of Client Type and QoS 109032.4.2 Positioning Method Selection 1091

The Positioning Sequence and Prior Performance Information 1091QoS Evaluation 1091

32.5 Coordinates 109332.5.1 Time 109332.5.2 Coordinate Systems 109332.5.3 Coordinate Transformations 1094

32.6 Positioning Methods in LTE 109632.6.1 Cell Identity (CID) 109732.6.2 E-CID 1097

CID and TA 1097Signal Strength 1098AOA 1100

32.6.3 Fingerprinting 1101RF Fingerprinting 1102AECID 1103

32.6.4 OTDOA 110632.6.5 U-TDOA 111232.6.6 A-GNSS 1113

32.7 Shape Conversion 111632.8 Positioning Performance in LTE 1118

32.8.1 Limiting Factors 111832.8.2 Accuracy Metrics 111932.8.3 Expected Performance 1119

CID 1119E-CID 1120Fingerprinting 1121OTDOA 1122U-TDOA 1122A-GNSS 1122Comparison of the Expected Performance for Different Methods 1123

32.9 Summary 1125 References 1125

CHAPTER 33 AUTOMATED WILDLIFE RADIO TRACKING 1129

33.1 Introduction 113033.2 A Review of Wildlife Tracking Techniques 1130

33.2.1 Wildlife Tag Design Constraints 113133.2.2 Terrestrial Wildlife Transmitters 1133

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33.2.3 Terrestrial Wildlife Receivers 1134Handheld Receivers 1134Automatic Receivers 1135

33.2.4 Satellite Tracking Systems 113633.2.5 Solar Geolocation Tracking 113733.2.6 Cellular Tracking 113833.2.7 Radar Tracking 113833.2.8 Summary and Motivation for Improvements 1139

33.3 A New Approach to Wildlife Tracking 113933.3.1 PRNs 1140

Chip Rate and Bandwidth 1143Detection via Matched Filters 1144

33.3.2 Signal Processing 1146Code Phase Search, Doppler Shift, and Frequency Error 1146Computational Requirements and Frequency Domain Operation 1148Time Shifting and Windowing 1149

33.3.3 System Description 1151Transmitters 1151Receiver Architecture 1152Time Base 1156

33.3.4 Arrival-Time Location-Finding Algorithms 1156Hyperbolic Positioning 1157Spherical Positioning 1158Iterative Root Finding (NR Method) 1158Stochastic Search (SS) Method 1161

33.4 Performance of a Demonstration Wildlife Tracking System 116233.5 Caveats and Limitations 116433.6 Conclusion 1165 References 1166

CHAPTER 34 AN INTRODUCTION TO THE FUNDAMENTALS AND IMPLEMENTATION OF WIRELESS LOCAL POSITIONING SYSTEMS 1169

34.1 Introduction 116934.2 WLPS Structure 117334.3 WLPS Performance Investigation 1178

34.3.1 The DS-CDMA Receiver 117934.3.2 Simulation Results 1180

34.4 Adaptive BF Techniques 118334.5 Novel DOA and TOA Estimation Algorithms 118634.6 WLPS Design and Structure 118734.7 Conclusions 1192 Acknowledgments 1193 References 1193

INDEX 1195

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