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HANDBOOK OF POSITION LOCATIONTheory, Practice, and Advances
Edited by
SEYED A. (REZA) ZEKAVAT and R. MICHAEL BUEHRER
A JOHN WILEY & SONS, INC., PUBLICATION
IEEE PRESS
HANDBOOK OF POSITION LOCATION
IEEE Press 445 Hoes Lane
Piscataway, NJ 08854
IEEE Press Editorial Board Lajos Hanzo, Editor in Chief
R. Abhari M. El - Hawary O. P. Malik J. Anderson B - M. Haemmerli S. Nahavandi G. W. Arnold M. Lanzerotti T. Samad F. Canavero D. Jacobson G. Zobrist
Kenneth Moore, Director of IEEE Book and Information Services (BIS)
A complete list of titles in the IEEE Press Series on Digital and Mobile Communication appears at the end of this book.
HANDBOOK OF POSITION LOCATIONTheory, Practice, and Advances
Edited by
SEYED A. (REZA) ZEKAVAT and R. MICHAEL BUEHRER
A JOHN WILEY & SONS, INC., PUBLICATION
IEEE PRESS
Copyright © 2012 by the Institute of Electrical and Electronics Engineers, Inc.
Published by John Wiley & Sons, Inc., Hoboken, New Jersey. All rights reserved.Published simultaneously in Canada
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Library of Congress Cataloging-in-Publication Data:
Position location : theory, practice, and advances / editors, Seyed A. (Reza) Zekavat, R. Michael Buehrer. p. cm. – (LEEE series on digital & mobile communication) ISBN 978-0-470-94342-7 (hardback) 1. Location-based services. 2. Mobile geographic information systems. 3. Wireless communication systems. 4. Electronics in navigation. I. Zekavat, Seyed A. II. Buehrer, R. Michael. TK5105.65.P665 2011 621.384'191–dc22 2011010970
Printed in Singapore
oBook ISBN: 9781118184750ePub ISBN: 9781118604767ePDF ISBN: 9781118104774
10 9 8 7 6 5 4 3 2 1
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
vi BRIEF CONTENTS
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
BRIEF CONTENTS vii
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
viii BRIEF CONTENTS
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 AustraliaBaris 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
Dragos Niculescu, University Politehnica of BucharestBdri Nath, Rutgers University
BRIEF CONTENTS ix
CHAPTER 27 SELF-LOCALIZATION OF UAV FORMATIONS USING BEARING MEASUREMENTS 899
Iman Shames, The Australian National University and National ICT AustraliaBaris 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
x BRIEF CONTENTS
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.
DETAILED CONTENTS
PREFACE xxxiii
CONTRIBUTORS xv
PART I FUNDAMENTALS OF POSITION LOCATION
CHAPTER 1 WIRELESS POSITIONING SYSTEMS: OPERATION, APPLICATION, AND COMPARISON 3
1.1 Introduction 3
1.2 Basic Methods Used in Positioning Systems 5
1.2.1 TOA Estimation 5
1.2.2 Time-Difference-of-Arrival (TDOA) Estimation 7
1.2.3 DOA Estimation 8
1.2.4 RSSI 8
1.2.5 LOS versus NLOS 8
1.2.6 Positioning, Mobility, and Tracking 8
1.2.7 Network Localization 9
1.3 Overview of Positioning Systems 9
1.3.1 GPS 9
Distance Measurement 10
Satellite Positions 12
1.3.2 Assisted Global Positioning System (AGPS or Assisted GPS) 12
1.3.3 INS 13
INS Classifi cation 14
1.3.4 Integrated INS and GPS 14
1.3.5 RFID 14
RFID as a Positioning System 15
1.3.6 WLPS 15
1.3.7 TCAS 17
1.3.8 WLAN 17
1.3.9 Vision Positioning System 18
1.3.10 Radar 18
1.4 Comparison of Basic Methods and Positioning Systems 18
1.5 Conclusion, Summary, and Future Applications 19
References 21
xi
xii DETAILED CONTENTS
CHAPTER 2 SOURCE LOCALIZATION: ALGORITHMS AND ANALYSIS 25
2.1 Introduction 26
2.2 Measurement Models and Principles for Source Localization 28
2.2.1 TOA 28
2.2.2 TDOA 30
2.2.3 RSS 31
2.2.4 DOA 33
2.3 Algorithms for Source Localization 34
2.3.1 Nonlinear Approaches 34
NLS 34
ML 40
2.3.2 Linear Approaches 44
LLS 44
WLLS 50
Subspace 53
2.4 Performance Analysis for Localization Algorithms 55
2.4.1 CRLB Computation 56
2.4.2 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 67
3.1.1 Why Is Location Security Important? 68
3.1.2 Defi nition of Position Location Security 69
3.1.3 Relationship to Network Security 69
3.2 Types of Position Location Attacks 69
3.2.1 APS 70
Modifi cation of Attack Position 70
Disruption of Attack Position 72
Recent Work 73
3.2.2 ASS 73
Modifi cation of Legitimate Position 74
Disruption of Legitimate Position 74
Recent Work 74
3.2.3 Location Disclosure 75
Recent Work 76
3.3 Impact and Analysis of Location Attacks 76
3.3.1 Adversary and Simulation Models 77
3.3.2 Optimality Criterion (Risk Measure) 80
3.3.3 Estimator Error Behavior under Attack 80
Impact of Location Attacks 81
Impact of Incorrect PL Estimation 83
3.3.4 Analysis of the Estimator Error Behavior 84
3.4 Attack Detection and Localization 86
3.4.1 Exploiting Geometric Features of Location Error 89
Residual Error Map and Node Convex Hull (NCH) 89
GF 92
DETAILED CONTENTS xiii
3.4.2 Attack Detection 92
Statistical Detection Technique 93
Geometric Pattern Matching for Attack Detection 95
Performance Evaluation 96
3.4.3 Adversary Localization 98
Noncooperative Position Location 98
Handling Position Outliers 99
Performance Evaluation 99
3.5 Conclusion and Continuing Work 102
References 102
CHAPTER 4 CHANNEL MODELING AND ITS IMPACT ON LOCALIZATION 105
4.1 Introduction 105
4.2 Channel Model 107
4.3 Important Statistics for Received Signal Strength (RSS) 109
4.4 Important Statistics for TOA, TDOA, and DOA 113
4.4.1 PDP Statistics and Impact on Localization and Radio Design 114
4.4.2 PSP Statistics and Impact on Localization and Radio Design 120
4.4.3 PAP Statistics and Impact on Localization and Radio Design 124
4.5 Summary of Different Channel Categories 125
4.6 Statistics of Amplitude, Phase, and TOA 126
4.6.1 Fade Amplitude 126
4.6.2 Fade-Phase Statistics 127
4.6.3 TOA 128
4.7 Other Channel Models 129
4.7.1 Geometric-Based Single-Bounce Statistical Channel Modeling 129
4.7.2 Circular and Elliptical Geometric Models 129
4.7.3 Rough Surface Channel Modeling 130
4.7.4 Near-Ground Channel Modeling 130
4.7.5 Foliage Effects 132
4.8 Conclusions 133
Acknowledgments 133
References 133
CHAPTER 5 COMPUTATIONAL METHODS FOR LOCALIZATION 137
5.1 Importance of Channel Modeling 137
5.2 Important Channel Model Parameters for Localization 140
5.3 TOA-Based Techniques 142
5.3.1 Challenges for TOA Techniques 142
5.3.2 Simulation and Measurement Techniques 144
5.3.3 Channel Measurement Technology 146
5.3.4 RT Algorithm 147
5.3.5 FDTD Method 148
5.4 Computational Method and the Effect of Micrometals 151
5.4.1 FDTD and the Effects of Micrometals 151
5.4.2 2-D FDTD Simulation Scenarios 153
5.4.3 Comparison of Computation with Empirical Results 156
5.4.4 Ray Optics and Effects of Micrometals 157
Analysis of Diffraction around the Edges 159
Comparison of Computation with Empirical Results 160
xiv DETAILED CONTENTS
5.5 FDTD and the Effects of the Human Body 163
5.5.1 Measurement of Wideband Characteristics 164
5.5.2 Computational Analysis of the Effects of the Human Body 166
An Overview of Ansoft HFSS 167
Analysis of Path Loss Models 167
Experimental 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 175
6.2 TDOA Positioning 176
6.2.1 Geometric Interpretation 177
6.2.2 Uplink versus Downlink Measurements 180
6.3 TOA Positioning 180
6.3.1 Geometric Interpretation 181
6.4 TDOA versus TOA 183
6.5 TOA versus TDOA in the Presence of Noise 184
6.6 Linearization 187
6.6.1 Taylor Series Approximation 187
6.6.2 Differencing 189
6.6.3 Linearization of TDOA 196
6.7 Pseudorange 196
6.8 The Impact of NLOS Propagation 199
6.8.1 Impact of NLOS Bias Errors 199
6.8.2 Discarding NLOS Range Estimates 200
6.8.3 NLOS Identifi cation 202
6.8.4 NLOS Mitigation 205
6.9 Handling NLOS Errors: a Linear Programming Approach 206
6.9.1 LOS Range Estimates 206
6.9.2 NLOS Range Estimates 207
6.9.3 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 213
7.2 TOA Estimation Methods 216
7.2.1 Conventional Correlation-Based Techniques 220
Pros and Cons 221
7.2.2 Deconvolution Methods 222
Pros and Cons 224
DETAILED CONTENTS xv
7.2.3 ML-Based Methods 225
Pros and Cons 226
7.2.4 Subspace-Based Techniques 226
Pros and Cons 228
7.2.5 BSS-Based Algorithms 229
Pros and Cons 233
7.3 Comparison of TOA Estimation Techniques 233
7.4 Range Estimation System Design 235
7.4.1 Single-Band Range Estimation Architecture 235
7.4.2 Multiband Range Estimation: General Architecture 236
7.4.3 Noncontiguous Multiband Scenario 238
7.5 Conclusion 240
References 240
CHAPTER 8 WIRELESS LOCALIZATION USING ULTRA-WIDEBAND SIGNALS 245
8.1 Introduction to UWB 245
8.1.1 Regularization 245
8.1.2 Transmission Approaches 246
8.1.3 Standards 247
8.1.4 UWB Channels 248
8.2 UWB Localization Techniques 250
8.2.1 Fingerprinting Localization 250
8.2.2 Geometric Localization 252
TOA Estimation 253
Position Estimation 253
8.2.3 NLOS Issues 254
8.3 TOA Estimation for IR UWB 255
8.3.1 System Model 255
8.3.2 ML TOA Estimation 257
8.3.3 Energy Detection-Based TOA Estimation 258
8.3.4 TDT 260
8.3.5 Discussions on IR-Based TOA Estimation 262
8.4 TOA Estimation for MB-OFDM UWB 263
8.4.1 System Model 265
8.4.2 Correlation-Based TOA Estimator 266
8.4.3 Energy Detection-Based TOA Estimator 267
8.4.4 TOA Estimation by Suppressing Energy Leakage 269
8.4.5 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 279
9.2 Antennas and Their Parameters 280
9.2.1 Antenna HPBW 282
9.2.2 First Side Lobe to the Main Lobe Power Ratio 283
9.2.3 Non-Main Lobe Power (All Side Lobe Power) to Main Lobe Power Ratio 283
xvi DETAILED CONTENTS
9.2.4 Antenna Impedance 283
9.2.5 Antenna Return Loss 284
9.2.6 Antenna Bandwidth 285
9.2.7 Antenna Gain 285
Antenna Gain Is Usually Measured in dBi 286
9.2.8 Antenna Polarization 287
9.3 Antenna Arrays 287
9.3.1 Smart Antennas 288
9.3.2 Important Parameters of Antenna Arrays 289
Array Vector 289
Array Factor 290
Mutual Coupling 291
9.4 DOA Estimation Methods 293
9.4.1 DAS 297
9.4.2 MUSIC and Root MUSIC 299
MUSIC 299
Root MUSIC 301
Complexity Analysis 302
Comparison of MUSIC and Root MUSIC 304
9.4.3 DAS and Root MUSIC Fusion 306
Simulations and Performance Analysis 308
9.4.4 Comparison 309
9.5 DOA Estimation for Periodic Sense Transmission 310
9.6 Conclusion 315
Acknowledgments 315
References 316
CHAPTER 10 SMART ANTENNAS FOR DIRECTION-OF-ARRIVAL INDOOR POSITIONING APPLICATIONS 319
10.1 Introduction 319
10.2 Principles of Indoor Positioning Based on SA 321
10.2.1 Positioning Estimation Techniques 321
10.2.2 DOA Principle of Operations 323
10.3 Antenna Technology and Design Principles 326
10.3.1 Radiation Pattern 326
10.3.2 Circular Polarization 328
10.3.3 Antenna Selector 328
10.3.4 Signal Detection Circuit 329
10.4 DOA Estimation Accuracy for SAs 330
10.4.1 Information Theory Elements 330
10.4.2 Derivation of the CRB for 1-D Case Using SAs 331
Effect of Number of Antenna Elements Nr 335
Effect of the Directivity Coeffi cient m 335
Effect of the RSSI Variance σ 2RSSI 336
10.4.3 Derivation of the CRB for 2-D DOA Using SAs 337
10.5 Algorithm for Indoor DOA Estimations 340
10.5.1 1-D DOA Estimation Methods 340
Strongest RSSI–Sector Partition 341
LSE 341
The MUSIC Estimator 343
DETAILED CONTENTS xvii
10.5.2 2-D DOA Estimation Methods 344
10.5.3 2-D DOA Simulated Experiments 345
10.6 Prototype of SA Suitable for Indoor DOA Positioning Applications 346
10.6.1 Six Switching Beams Antenna Prototype: Characteristics and Performance 346
10.6.2 Prototype DOA Estimation Performance 349
Strongest RSSI 350
Fingerprinting 351
MUSIC 352
10.6.3 Experimental Results and Conclusions 352
10.7 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 359
11.1.1 Why Is RSS Attractive for Localization? 360
11.1.2 Problem Statement and Outline 360
11.2 Sources of Location Error and Mitigation 362
11.2.1 Multipath Fading and NLOS Propagation 362
11.2.2 Shadow Fading 363
11.2.3 Systematic Bias or Error 363
11.2.4 Geometric Node Confi guration 363
11.3 Techniques Using RSS for Position Location 363
11.3.1 Range-Based Positioning 364
Statistical Model for RSS 364
Basics of Differential RSS 365
Spatial Correlation of Shadow Fading 367
11.3.2 RF Fingerprinting 368
11.3.3 Proximity-Based Positioning 370
Dimensionality Reduction Using Geographical Proximity 370
11.4 Geometric Interpretations of RSS/DRSS Positioning 372
11.4.1 RSS-Based Lateration 375
11.4.2 DRSS-Based Lateration 377
Geometry of Relative DRSS Positioning 377
Geometry of Absolute DRSS Positioning 379
Geometric Solution of DRSS Location 380
11.5 Location Estimators 380
11.5.1 Theoretical Limits for Location Estimation 381
Optimality Criterion 381
Cramer–Rao Lower Bound (CRLB) 381
11.5.2 ML Estimator 382
11.5.3 Nonlinear LS Estimator 383
LS Optimization Framework 383
11.5.4 Linear LS Estimator 385
xviii DETAILED CONTENTS
11.6 Performance Evaluation 387
11.6.1 Simulation Settings 387
Numerical Optimization Algorithm Considered 388
11.6.2 Simulation Results 388
Impact of Number of Anchor Nodes and Spatial Correlation 388
Impact of Correlated Shadow Fading 389
Impact 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 396
12.2 RSS-based Localization Algorithms 397
12.2.1 Approach Overview 398
12.2.2 Lateration Methods 399
NLS 399
LLS 400
12.2.3 Classifi cation via Machine Learning 401
12.2.4 Probabilistic Approaches 403
12.2.5 Statistical Supervised Learning Techniques 404
12.2.6 Summary of Localization Algorithms 405
12.3 Localization Performance Study 407
12.3.1 Performance Metrics 407
12.3.2 Performance Investigation Using Real Wireless Networks 408
Experimental Scenarios 408
Performance Results 410
12.4 Enhancing the Robustness of Localization 413
12.4.1 Real-Time Infrastructure Calibration 413
12.4.2 Effects of Employing Multiple Antennas 414
12.4.3 Robust Statistical Methods 416
12.4.4 Revisiting Linear Regression 417
12.4.5 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 425
13.2 Anchor Placement 426
13.2.1 Overview 426
13.2.2 Impact of Anchor Placement 428
13.2.3 Heuristic Search 431
13.2.4 Acute Triangular-Based Deployment 433
13.2.5 Adaptive Beacon Placement 435
13.2.6 Optimal Placement via maxL–minE 436
Theoretical Analysis 436
Algorithm Overview and Experimental Evaluation 441
DETAILED CONTENTS xix
13.3 Anchor Selection 445
13.3.1 Overview 445
13.3.2 Joint Clustering Technique 445
13.3.3 Entropy-Based Information Gain 447
13.3.4 Convex Hull Selection 447
13.3.5 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 457
14.1.1 Outline of the Chapter 459
14.2 Kernel Methods 459
14.2.1 Problem Statement 460
14.2.2 General Mathematical Formulation 460
Determination of Kernel Parameters 461
Example Framework 462
14.2.3 LANDMARC Algorithm 464
Estimation of Parameters 464
14.2.4 Gaussian Kernel Localization Algorithm 465
Estimation of Parameters 466
14.2.5 Radial Basis Function-Based Localization Algorithm 468
Estimation of Parameters 469
14.2.6 Linear Signal-Distance Map Localization Algorithm 470
Estimation of Parameters 472
14.2.7 Summary 473
14.3 Numerical Examples 473
14.3.1 MLE 473
Estimating Coordinate from RSS 474
Implementation Details 474
14.3.2 Description of Comparison Example 475
14.4 Evaluation Using Measurement Data Set 481
14.4.1 Measurement Campaign Description 481
14.4.2 Evaluation Procedure 481
14.4.3 Results 482
Bias Results 482
RMSE Results 484
14.5 Discussion and Conclusion 484
References 485
CHAPTER 15 RF FINGERPRINTING LOCATION TECHNIQUES 487
15.1 Introduction 487
15.2 RF Fingerprints 489
15.3 CDB 490
15.3.1 CDB Structure 490
Uniform Grid 491
Indexed List 491
xx DETAILED CONTENTS
15.3.2 Building the CDB 491
Field Measurements 491
Propagation Modeling 492
Mixing Predicted and Measured Values 498
15.4 Techniques to Reduce the Search Space 499
15.4.1 CDB Filtering 500
First Filtering Step 500
Second Filtering Step 501
Third Filtering Step 501
15.4.2 Optimized Search Using GAs 502
15.5 Pattern Matching of RF Fingerprints 504
15.5.1 Distance in N-Dimensional RSS Space 505
Particular Case 505
Generic Case with Penalty Term 506
15.5.2 Pattern Matching Using ANNs 508
15.5.3 Spearman Rank Correlation Coeffi cient 510
15.6 Experimental Performance 512
15.6.1 Outdoor 850-MHz GSM Network 512
15.6.2 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 524
16.2 NLOS Identifi cation 525
16.2.1 Cooperative Methods 527
DOA Residual Testing 527
Time-Difference-of-Arrival (TDOA) Residual 528
Residual Distribution Testing 529
16.2.2 Single-Node Methods Based on the Range Statistics 530
Techniques Based on Range Measurements Over Time 530
Techniques Based on the Range Measurements over Different Frequency Bands 531
16.2.3 Single-Node Methods Based on Channel Characteristics 532
Narrow and Wideband Systems 533
UWB Systems 534
Systems Using Antenna Array 536
16.2.4 Single-Node Hybrid Approach 541
16.2.5 Comparison of NLOS Identifi cation Methods 543
16.3 NLOS Localization 543
16.3.1 RSSI 544
16.3.2 Bidirectional TOA–DOA Fusion 546
16.3.3 Single BN TOA–DOA Fusion with the Assistant Environment Map 547
DETAILED CONTENTS xxi
16.3.4 Multinode TOA–DOA Fusion 548
16.3.5 Comparison 550
16.4 Conclusion 552
References 552
CHAPTER 17 NLOS MITIGATION METHODS FOR GEOLOCATION 557
17.1 Introduction 558
17.2 Geolocation System Model 559
17.3 A Review of NLOS Mitigation Techniques 560
17.3.1 ML-Based Techniques 560
Finding Nh ML Estimates of Unknown Parameters 562
Finding the Most Possible Hypothesis 562
17.3.2 LS-Based Techniques 562
17.3.3 Constrained Optimization Techniques 564
17.3.4 Robust Estimator Techniques 565
17.4 Application of the Single Moving Sensor Geolocation 566
17.4.1 Range Measurements Profi le-Based Trimming 567
17.4.2 Reconstruction of Trimmed TOA Profi le 571
17.4.3 Robust Trimming with Nonparametric Noise Density Estimator 572
17.4.4 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 584
18.1.1 Background 584
18.1.2 Literature Review 584
LOS/NLOS Detection 585
Wireless Geolocation 586
18.1.3 Merits 587
18.1.4 Organization 587
18.2 System Model 588
18.2.1 Existing Techniques for Mobile Position Estimation 588
LLS Based on First-Order Taylor Series 589
LLS with Additional Parameterization 590
AML 591
18.2.2 Path Loss Model 593
18.3 Mobile Position Estimation 594
18.3.1 TOA Estimation 594
LOS Suffi ciency 595
18.3.2 LS 596
18.3.3 WLS 596
18.3.4 ML 596
LS Error Variance 596
18.4 CRB for Mobile Position Estimation 597
18.4.1 FIM of TOA Estimation 597
18.4.2 CRB for TOA Estimation 598
18.4.3 CRB for Mobile Position Estimation 598
xxii DETAILED CONTENTS
18.5 Numerical Examples 598
18.6 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 629
19.2 The Estimation Problem 631
19.3 Formulation of Localization as an Estimation Problem 632
19.4 Discrete Linear Kalman Filter 633
19.4.1 Kalman Filter Derivation 633
19.4.2 Discussion and Implementation 635
19.5 Continuous Kalman Filter 641
19.6 Extended Kalman Filter 643
19.7 Further Reading 646
References 646
CHAPTER 20 REMOTE SENSING TECHNOLOGIES FOR INDOOR APPLICATIONS 649
20.1 Position Sensing Technology 650
20.1.1 Vision-Based Position Sensors 650
20.1.2 Non-Vision-Based Position Sensor 653
20.1.3 Inertial Sensors 657
Orientation Calculation Using Quaternion 657
Position Calculation Using Inertial Sensors 660
IMU 662
20.1.4 Applications 665
20.2 Bayesian Estimators 667
20.2.1 Bayes Filter 668
20.2.2 KF 670
20.2.3 Extended KF 671
20.2.4 PF 673
20.2.5 Filter Comparison Example 675
20.2.6 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 685
21.2 System Description 686
21.2.1 General Problem Formulation 686
21.2.2 Example of the State Model 688
21.2.3 Example of the Measurement Model 688
DETAILED CONTENTS xxiii
21.3 Tracking Algorithm Based on GMF 689
21.3.1 The Development of GMF 689
Forgetting Components 692
Merging Components 692
Convergence Result of GMF 693
21.3.2 The Modifi ed EKF Banks 693
Algorithm Description 693
21.4 Tracking Method Based on ARBPF 695
21.4.1 Generic PF 695
21.4.2 Approximated RBPF 696
21.5 Lower Bound of Performance 699
21.6 Numerical Results 702
21.6.1 Performance Comparison with Different Algorithms 703
21.6.2 Comparison with Posterior CRLB 704
21.6.3 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 710
22.2 Review of Kalman Filtering and Extended Kalman Filtering for Navigation 711
22.2.1 State-Space Models 711
22.2.2 Continuous Time to Discrete-Time Transformation 714
22.2.3 Recursive Estimation and Initial Conditions 716
22.2.4 Extended KF 718
Linearized and Extended Architectures 720
22.3 EKF-Based PVT Computation in a Stand-Alone GNSS Receiver 721
22.3.1 State-Space Model 722
22.3.2 Linearization of the Measurement Equation 724
Pseudorange and Pseudorange Rate Prediction 726
22.3.3 Error Covariance Matrices 727
22.4 Inertial Navigation Fundamentals 728
22.4.1 Structure of an IMU 729
22.4.2 The Coriolis Theorem 730
22.4.3 Mechanization Equations 730
Computation and Tracking of the Body Attitude: The Direction Cosine Matrix (DCM) 731
Computation and Tracking of the Velocity 732
Computation and Tracking of the Position 732
22.5 IMU Alignment 733
22.5.1 GNSS-INS Hybridization: State-Space Models 735
22.6 General Architecture for the Loose Integration 735
22.6.1 Loose Integration: State-Space Model 735
Space Equation 736
Velocity Equation 737
Attitude Misalignment Equation 738
Accelerometers Bias Equation 739
Gyroscopes Bias Equation 739
xxiv DETAILED CONTENTS
22.6.2 Loose Integration: State Transition Matrix 740
22.6.3 Loose Integration: Measurement Equation 741
22.7 General Architecture for the Tight Integration 741
22.7.1 Tight Integration: State-Space Model 742
Clock Misalignment Equation 743
Clock Drift Equation 743
22.7.2 Tight Integration: State Transition Matrix 743
22.7.3 Tight Integration: Measurement Equation 744
22.8 General Architecture for the Ultra-Tight Integration 745
22.8.1 Ultra-Tight Integration: State-Space Model 746
22.8.2 Ultra-Tight Integration: State Transition Matrix 746
22.8.3 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 755
23.2 Problem Defi nition 758
23.3 Performance Bounds 760
23.3.1 CRLB 760
23.3.2 MLE/Weighted LS 763
The Branch-and-Bound (BB)/Reformulation-Linearization Technique (RLT) Algorithm 764
Reformulation and Linearization of the MLE 765
Partitioning Variables, Relaxation Errors, and Partitioning Strategies 768
23.3.3 Numerical Results 768
23.4 An Overview of Suboptimal Algorithms 771
23.4.1 A Taxonomy of Existing Algorithms 774
Type of Measurement Data: Distance, Angle of Arrival (AOA), and RSS Fingerprinting 774
Where the Computation Is Performed: Centralized or Distributed 774
How the Computation Is Performed: Sequential or Concurrent 774
How the Problem Is Formulated: Probabilistic or Nonprobabilistic 775
23.5 Specifi c Suboptimal Approaches 775
23.5.1 Sequential LS 776
23.5.2 Optimization-Based Approaches 778
23.5.3 MDS 780
23.5.4 Set-Theoretic Approach: Iterative Parallel Projection Method (IPPM) 783
The Modifi ed Parallel Projection Method (MPPM) 783
IPPM for Collaborative Position Location 788
23.6 Numerical Comparison of Approaches 793
23.6.1 Localization Accuracy 793
23.6.2 Computational Complexity 799
DETAILED CONTENTS xxv
23.7 NLOS Propagation 800
23.7.1 Knowledge about the NLOS Propagation 801
23.7.2 NLOS Mitigation Example 801
23.7.3 Simulation Results 803
23.8 Summary 807
References 808
CHAPTER 24 POLYNOMIAL-BASED METHODS FOR LOCALIZATION IN MULTIAGENT SYSTEMS 813
24.1 Introduction 813
24.2 Polynomial Function Optimization 815
24.2.1 Polynomial Continuation (Homotopy) Methods 816
24.2.2 SOS and SDP Approaches 817
24.3 Noisy Target Localization 819
24.4 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 826
24.4.4 Algorithmic Comparison with Some Existing Methods 829
Comments on the Complexity of SOS Methods 830
24.4.5 Colinear Anchors 831
24.5 An Extension of the SOS Approach 832
24.6 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 838
25.1.1 Classifi cation of Cooperative Localization Methods 838
Range-Based versus Range-Free Methods 838
Centralized versus Distributed Methods 839
Anchor-Based versus Anchor-Free Methods 839
Probabilistic versus Deterministic Methods 839
25.1.2 Measurement Techniques 840
25.1.3 Motivating Applications 841
25.2 Probabilistic Localization Based on BP 842
25.2.1 Introduction to Probabilistic Localization 842
Statistical Framework for Probabilistic Localization 842
25.2.2 Belief Propagation 843
Graphical Model 844
Description of the Algorithm 847
25.2.3 NBP 848
Computing Messages 848
Computing Beliefs 849
Convergence of NBP 850
xxvi DETAILED CONTENTS
25.2.4 NBBP 850
Modifi cations 850
Performance Analysis 851
25.3 Generalized BP Methods 855
25.3.1 Correctness of BP 856
25.3.2 GBP-K 857
25.3.3 NGBP-JT 857
Defi nition 857
Example Network 858
Nonparametric Approximation 860
25.3.4 NBP-ST 861
Spanning Tree Formation 861
Performance Analysis 864
25.4 Conclusions 867
Acknowledgments 867
References 867
CHAPTER 26 ERROR CHARACTERISTICS OF AD HOC POSITIONING SYSTEMS 871
26.1 Introduction 871
26.2 APS Algorithms 873
26.2.1 DV-Hop Propagation Method 874
26.2.2 DV-Euclidean and DV-Radial 876
26.2.3 DV-Position 877
26.3 Positioning Error Analysis 878
26.3.1 Trilateration Review 878
26.3.2 CRLB for Trilateration 879
26.3.3 DV-Hop Range Error 879
26.3.4 CRLB for DV-Hop Positioning 883
26.3.5 DV-Position Error 885
26.4 Discussion 888
26.5 Related Work 890
26.6 Conclusion 891
References 891
Appendices 892
CHAPTER 27 SELF-LOCALIZATION OF FORMATIONS OF AUTONOMOUS AGENTS USING BEARING MEASUREMENTS 899
27.1 Introduction 899
27.2 Problem Setup 901
27.3 A Rigid Graph Theoretical Framework for Formation Localization 903
27.4 Four-Bar Linkage Mechanisms 906
27.5 A Localization Algorithm Based on Four-Bar Linkage Mechanisms 908
27.6 Localization of Larger Formations 914
27.7 Localization with Extra Landmarks 916
27.8 Availability of More Angle Measurements for Three Agents 917
27.9 Conclusions 918
Acknowledgments 919
References 919
DETAILED CONTENTS xxvii
PART VII APPLICATIONS
CHAPTER 28 OVERVIEW OF GLOBAL NAVIGATION SATELLITE SYSTEMS 923
28.1 Introduction 923
28.1.1 What Is Radio Navigation? 924
28.1.2 Spherical Systems 924
Two-Way Measurements 925
One-Way Measurement 926
28.1.3 Evolution Programs of GNSS Constellations 926
28.2 Principles of Satellite Navigation 927
28.2.1 Geometry and Measurement Errors 929
28.2.2 Impact of Measurement Errors on User Position 930
28.3 The Impact of Geometry 932
28.3.1 GDOP as a Function of Position and Time 934
28.4 Overview on Reference Systems 939
28.4.1 Conventional Inertial Reference System 939
28.4.2 Conventional Terrestrial Reference System 940
28.4.3 Ellipsoidal Coordinates 941
28.4.4 The Geoid 942
28.4.5 The Global Datum 942
28.4.6 East-North-Up (ENU) Reference Frame 943
28.5 Structure of the Signal In Space (SIS) 943
28.5.1 GNSS Frequency Plan 944
28.5.2 The Binary Offset Carrier (BOC) Modulation 944
BOC Power Spectral Density 946
Correlation Properties 946
BOC versus BOCcos 948
28.5.3 The GNSS Transmitted Signal 949
28.6 Current and Modernized GPS Signals 950
28.6.1 Multiplexed BOC (MBOC) Signal Baseline 951
28.6.2 TMBOC Modulation 952
28.7 Galileo System and SIS 953
28.7.1 E1 CBOC Modulation 954
28.7.2 CASM Multiplexing Scheme 958
28.7.3 AltBOC Modulation and Multiplexing Scheme 960
The AltBOC Concept 961
28.8 Error Sources for the Position Evaluation 965
28.8.1 GNSS Positioning 966
Impact of Ranging Errors on Position Metrics 966
28.9 Augmentations 968
28.9.1 Local Area Differential Corrections 968
28.9.2 Wide Area Differential Corrections 969
The Integrity Concept 970
28.9.3 A-GNSS and Cooperative Navigation 971
28.9.4 Trend of GNSS-Related Augmentation Solutions and Technologies 972
28.10 Conclusions 972
Acknowledgment 973
References 973
xxviii DETAILED CONTENTS
CHAPTER 29 DIGITAL SIGNAL PROCESSING IN GNSS RECEIVERS 975
29.1 Received Signal 976
29.1.1 The Doppler Effect in the Carrier 977
29.1.2 The Doppler Effect at Baseband 978
29.2 The General Receiver Structure 978
29.2.1 Sampling Frequency 979
29.2.2 The Digital IF Signal 980
Carrier-to-Noise Ratio and Signal-to-Noise Ratio (SNR) 980
29.3 Acquisition 985
29.3.1 Detection and Estimation Main Strategy 986
Parameter Estimation 986
Detection 987
29.3.2 Cross-Ambiguity Function (CAF) 988
The SS 989
Consideration on the Value of the Frequency Bin Size 990
Consideration on the Value of the Delay Bin Size 992
SNR at the CAF Peak 992
Coherent and Noncoherent Integration 993
29.3.3 Refi nement of the Estimation of the SIS Parameters 994
29.4 The Role of FFT in a GNSS Receiver 996
29.4.1 FFT in the Time Domain 997
29.4.2 FFT in the Doppler Domain 998
29.5 Estimation of the Propagation Delay 999
29.6 Methods for SIS Detection 1000
29.6.1 NP Approach 1000
NP Detection in GNSS 1002
29.6.2 Detection Based on the A Posteriori Probabilities 1003
29.6.3 Bayesian Sequential Detection 1003
Sequential Detection in GNSS 1005
29.7 Gradient Method for SIS Parameters Estimation 1006
29.7.1 Transient between Signal Acquisition and Tracking 1006
29.7.2 Fundamentals on the Gradient Theory 1007
29.7.3 Application to GNSS Signals 1009
29.8 Null Seeker and Tracking Loops 1011
29.8.1 DLL 1013
Discrimination Function 1014
29.8.2 Carrier Tracking 1016
29.8.3 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 1023
30.1.1 Basic Navigation Concepts 1023
30.1.2 Estimating Robot’s Pose 1026
30.1.3 Experimental Verifi cation: Grid-Like Pattern 1028
