Academic Press Library Signal Processing - gbv.de · Dipartimento diIngegneria dell'lnformazione,...

Academic Press Library in

Signal Processing

Volume 2

Communications and Radar

Signal Processing

Editors

Nicholas D. SidiropoulosDept. of Electrical and Computer Engineering,

University of Minnesota, Minneapolis, MN, USA

Fulvio Gini

Dipartimento di Ingegneria dell'lnformazione,

University of Pisa, Pisa, Italy

Rama ChellappaDepartment of Electrical and Computer Engineering and

Center for Automation Research,

University of Maryland,

College Park, MD, USA

Sergios Theodoridis

Department of Informatics & Telecommunications,

University of Athens, Greece

ELSEVIER

AMSTERDAM • WALTHAM • HEIDELBERG • LONDON

NEW YORK • OXFORD • PARIS • SAN DIEGO

SAN FRANCISCO • SYDNEY • TOKYO

Academic Press is an imprint of Elsevier

Contents

Introduction xxiii

About the Editors xxvii

Section Editors xxix

Authors Biography xxxi

SECTION I SIGNAL PROCESSING FOR COMMUNICATIONS MODULES

CHAPTER 1 Introduction to Signal Processing for Communications 3

Nicholas D. Sidiropoulos

2.01.1 Some history 3

2.01.2 Contents and contributors 4

2.01.2.1 Synchronization 4

2.01.2.2 Channel estimation, equalization, precoding, and tracking 4

2.01.2.3 Blind signal separation 5

2.01.2.4 OFDM and multicarrier signal processing 5

2.01.2.5 Signal processing for vectored multichannel VDSL 5

2.01.2.6 Distributed detection and estimation in wireless sensor networks 5

2.01.2.7 Signal processing and optimal resource allocation for the

interference channel 6

2.01.2.8 Advances in spectrum sensing and cross-layer design for

cognitive radio networks 6

2.01.3 Outlook 6

References 6

CHAPTER 2 Synchronization 9

Mounir Ghogho, Philippe Ciblat and Ananthram Swami

2.02.1 Introduction 9

2.02.2 Synchronization in flat fading channel 11

2.02.2.1 DA case 18

2.02.2.2 NDA case 31

2.02.3 Synchronization for non-flat fading channels 49

2.02.3.1 Signal model and preliminaries 50

2.02.3.2 Downlink OFDMA 57

2.02.4 Multiuser synchronization 68

2.02.4.1 Uplink signal model and synchronization policy 68

V

vi Contents

2.02.4.2 Synchronization with subband CAS 70

2.02.4.3 Synchronization with interleaved CAS 71

2.02.4.4 Synchronization with generalized CAS 74

2.02.4.5 Simulation results for uplink OFDMA systems 77

2.02.5 Network synchronization 79

2.02.5.1 Clock models 81

2.02.5.2 Net sync protocols 82

2.02.5.3 Distributed clock sync 85

2.02.6 Conclusion 86

References 86

CHAPTER 3 Channel Estimation, Equalization, Precoding,and Tracking 95

Jitendra K. Tugnait


2.03.2 Channel models 96

2.03.2.1 Time-variant (doubly selective) channels 96

2.03.2.2 Time-invariant channels 100

2.03.2.3 MIMO channels 102

2.03.3 Channel estimation 102

2.03.3.1 Training-based channel estimation 103

2.03.3.2 Blind channel estimation 104

2.03.3.3 Semi-blind approaches 107

2.03.3.4 Superimposed training-based approaches 107

2.03.3.5 MIMO channel estimation 108

2.03.4 Equalization 108

2.03.4.1 Linear equalization 108

2.03.4.2 Decision feedback equalization 111

2.03.4.3 Maximum likelihood sequence detection 113

2.03.4.4 Turbo equalization 114

2.03.5 Precoding 116

2.03.5.1 Precoding for SISO channels 116

2.03.5.2 Precoding for MIMO channels 117

2.03.5.3 Precoding with partial or no CSI at transmitter 117

2.03.6 Tracking 118

2.03.6.1 Adaptive channel estimation for slowly varying channels 118

2.03.6.2 Block-adaptive channel estimation using CE-BEM 118

2.03.6.3 Adaptive channel estimation via subblock tracking 119

2.03.6.4 Symbol-adaptive joint channel estimation and

data detection 120

Contents vii


References 129

CHAPTER 4 Blind Signal Separation for Digital Communication Data 135

Antoine Chevreuil and Philippe Loubaton


2.04.1.1 Generalities on blind source separation 135

2.04.1.2 Illustration of the potential of BSS techniques for communication

signals 136

2.04.1.3 Organisation of the paper 138

2.04.2 Signals 141

2.04.2.1 Source signals. Basic assumptions 142

2.04.2.2 Cyclo-stationarity of a source 143

2.04.2.3 Received signals 145

2.04.3 Instantaneous mixtures 148

2.04.3.1 Indeterminacies 149

2.04.3.2 Algebraic methods (i.i.d. scenario) 149

2.04.3.3 Second-order based identification (general cyclo-stationary case) 150

2.04.3.4 Iterative BSS (stationary case) 151

2.04.3.5 Global BSS (stationary mixture) 158

2.04.3.6 Generalizations 162

2.04.3.7 Iterative BSS (general cyclo-stationary case) 163

2.04.3.8 Global BSS (general cyclo-stationary case) 168

2.04.3.9 Validity of assumptions A\ and Ar. semi-analytical

considerations 169

2.04.4 Convolutive mixtures: case of sparse channels 171

2.04.5 Convolutive mixtures 173

2.04.5.1 Identifying the symbols: algebraic methods (stationary data) 173

2.04.5.2 Estimation of the channels: MA/AR structures (stationary data) 174

2.04.5.3 Estimation of the channels: subpace methods

(cyclo-stationary data) 174

2.04.5.4 Global BSS approaches 175

2.04.5.5 Iterative BSS (stationary case) 176

2.04.5.6 Iterative BSS (general cyclo-stationary) 178

2.04.6 Simulation 180

2.04.7 Extensions and further readings 182

2.04.7.1 Case of non-circular sources 182

2.04.7.2 Exploiting the non-stationarity 182

2.04.7.3 Presence of additive noise 182


References 183

viii Contents

CHAPTER 5 OFDM and Multicarrier Signal Processing 187

Paolo Banelli and Luca Rugini


2.05.2 Mathematical background 189

2.05.3 Single carrier background 189

2.05.4 The path to OFDM 193

2.05.4.1 Frequency division multiplexing 193

2.05.4.2 Orthogonal multiplexing principle 194

2.05.4.3 Orthogonal frequency-division multiplexing principle 196

2.05.4.4 DFT-based implementation of OFDM 198

2.05.5 OFDM in frequency-selective multipath channels 200

2.05.5.1 Zero-padded OFDM (ZP-OFDM) 202

2.05.5.2 Cyclic-prefix OFDM (CP-OFDM) 204

2.05.6 A vector-matrix representation for OFDM 207

2.05.6.1 Vector-matrix representation of CP-OFDM 209

2.05.6.2 A note on the CP philosophy 211

2.05.6.3 Vector-matrix representation of ZP-OFDM 212

2.05.7 Symbol error rate (SER) performance analysis 214

2.05.7.1 Suboptimal linear equalization/detection for ZP-OFDM 218

2.05.8 Coded—OFDM 218

2.05.9 Linearly precoded-OFDM (LP-OFDM) 219

2.05.10 Multiuser multicarrier systems 224

2.05.10.1 Equalization and data recovery for multiuser multicarrier

systems 228

2.05.10.2 A note on CDMA versus MC communications 232

2.05.11 OFDM and multicarrier communications in nonlinear systems 233

2.05.11.1 PAPR reduction methods 241

2.05.12 Estimation of LTI channels for OFDM 243

2.05.13 OFDM propagation in rapidly time-varying channels 248

2.05.13.1 Carrier frequency offset (CFO) 252

2.05.13.2 LTV channel equalization 253

2.05.13.3 LTV channel estimation 255

2.05.14 Design of OFDM systems: practical guidelines and commercial

standards 260

2.05.15 OFDM time and frequency synchronization 263

2.05.16 Capacity for OFDM transmissions: bit and power-loading 269

2.05.17 Further research topics and directions 275

2.05.17.1 Power and bit loading with CSI information feedback 276

2.05.17.2 Time-varying channel estimation and equalization for

MIMO, and underwater communications 276

Contents ix

2.05.17.3 OFDM-based cognitive radio applications 276

2.05.17.4 Filterbank multicarriers 277

2.05.17.5 OFDM-based relay communications 277

References 279

Additional references 291

CHAPTER 6 Signal Processing for Vectored Multichannel VDSL 295

Itsik Bergel and Amir Leshem


2.06.2 System model 297

2.06.2.1 Channel matrix structure 301

2.06.3 Downstream transmission 304

2.06.3.1 Nonlinear precoding 304

2.06.3.2 Linear precoding 307

2.06.3.3 Adaptive precoding 314

2.06.4 Upstream transmission 322

2.06.4.1 Linear receiver 322

2.06.4.2 Decision feedback equalizer 323


References 325

CHAPTER 7 Distributed Detection and Estimation in WirelessSensor Networks 329

Sergio Barbarossa, Stefania Sardellitti and Paolo Di Lorenzo


2.07.2 General framework 332

2.07.2.1 Computing while communicating 332

2.07.2.2 Impact of observation model 336

2.07.2.3 Fundamental information-theoretical issues 341

2.07.2.4 Possible architectures 342

2.07.3 Graphical models and consensus algorithm 343

2.07.3.1 Consensus algorithm 344

2.07.3.2 Consensus algorithms over realistic channels 348

2.07.4 Distributed estimation 351

2.07.4.1 Decentralized observations with decentralized estimation 353

2.07.4.2 Decentralized observations with centralized estimation 367

2.07.5 Distributed detection 371

2.07.5.1 Nodes send data to decision center 376

x Contents

2.07.5.2 Nodes send local decisions to fusion center 380

2.07.6 Beyond consensus: distributed projection algorithms 381

2.07.7 Minimum energy consensus 385

2.07.7.1 Optimization criterion 387

2.07.7.2 Optimal topology and power allocation for arbitrary networks 388

2.07.7.3 Numerical examples 389

2.07.7.4 Minimization of the energy consumption over random

geometric graphs 391

2.07.8 Matching communication network topology to statistical dependency

graph 393

2.07.8.1 Encouraging sparsity by preserving total transmit power 394

2.07.8.2 Sparsification and estimation of the precision matrix 396

2.07.8.3 Numerical results 396

2.07.9 Conclusions and further developments 398

Appendix A 399

A.1 Algebraic graph theory 400

A.2 RGG adjacency matrix 403

References 405

CHAPTER 8 Signal Processing and Optimal Resource Allocation for the

Interference Channel 409

Mingyi Hong and Zhi-Quan Luo


2.08.1.1 Resource allocation in communication networks 409

2.08.1.2 Notation 410

2.08.1.3 Interference channels 410

2.08.1.4 System model 412

2.08.2 Information-theoretic results 414

2.08.2.1 Capacity results for IC model 414

2.08.2.2 Achievable rate regions when treating interference as noise 416

2.08.3 Optimal resource allocation in interference channel 420

2.08.3.1 Problem formulations 420

2.08.3.2 Complexity of the optimal resource allocation problems 424

2.08.3.3 Algorithms for optimal resource allocation 425

2.08.4 Distributed resource allocation in interference channel 445

2.08.4.1 Game theoretical formulations 447

2.08.4.2 Distributed resource allocation for interference channels 449

2.08.5 Resource allocation via interference alignment 458

References 462

Contents xi

CHAPTER 9 Advances in Spectrum Sensing and Cross-Layer Designfor Cognitive Radio Networks 471

Seung-Jun Kim, Emiliano Dall'Anese, Juan Andres Bazerque, Ketan Rajawat

and Georgios B. Giannakis


2.09.2 Sensing at the physical layer 473

2.09.2.1 Sparsity-aware power spectrum cartography 475

2.09.2.2 Channel gain cartography 480

2.09.3 Sensing at the MAC layer 486

2.09.3.1 Wireless regional area networks 486

2.09.3.2 Cellular networks: MAC sensing as an inference problem 487

2.09.4 CR Sensing and cross-layer design 489

2.09.4.1 Joint sensing and resource optimization 490

2.09.4.2 Cartography-enabled route optimization 493


Glossary 496

References 497

SECTION II RADAR SIGNAL PROCESSING

CHAPTER 10 Introduction to the Radar Signal Processing Section 505

Fulvio Gini

References 511

CHAPTER 11 Radar Clutter Modeling and Analysis 513

Maria S. Greco and Simon Watts


2.11.2 Clutter modeling 515

2.11.2.1 Generic clutter characteristics 515

2.11.2.2 Sea clutter 527

2.11.2.3 Ground clutter 534

2.11.2.4 Rain clutter 536

2.11.3 Radar clutter analysis 539

2.11.3.1 Sea clutter 540

2.11.3.2 Land clutter analysis: farmland area 567

2.11.4 Simulation methods 581

2.11.4.1 Generating uncorrelated random numbers with a prescribed PDF 581

2.11.4.2 Generating correlated Gaussian random numbers 582

xii Contents

2.11.4.3 Correlated non-Gaussian random numbers 582

2.11.5 Use of clutter models in radar design and analysis 585

2.11.5.1 Performance prediction 585

2.11.5.2 The specification and measurement of radar performance 587

2.11.6 Conclusions 589

References 590

CHAPTER 12 Space-Time Adaptive Processing for Radar 595

William L. Melvin


2.12.1.1 Historical overview 596

2.12.1.2 Organization 599

2.12.1.3 Key points 601

2.12.2 Basic concepts 602

2.12.2.1 Detection 602

2.12.2.2 Space-time filter formulations 603

2.12.2.3 Sample matrix inversion 609

2.12.2.4 Metrics 611

2.12.3 Signal models 615

2.12.3.1 Clutter 615

2.12.3.2 Radio frequency interference 619

2.12.3.3 Receiver noise 620

2.12.3.4 Target 620

2.12.3.5 The space-time snapshot 621

2.12.4 Adaptive filter implementations 622

2.12.4.1 Reduced-rank STAP 622

2.12.4.2 Reduced-dimension STAP 630

2.12.4.3 Parametric adaptive matched filter 635

2.12.4.4 Summary of adaptive filter implementations 636

2.12.5 Application 639

2.12.5.1 Interference characteristics 639

2.12.5.2 STAP algorithm performance 642

2.12.5.3 STAP Comparison with nonadaptive solution 644

2.12.5.4 Application summary 644

2.12.6 Challenges 645

2.12.6.1 Heterogeneous clutter 645

2.12.6.2 Nonstationary clutter 647

2.12.6.3 Bistatic STAP 649

2.12.6.4 Conformal array STAP 650

2.12.7 Implementation ' 651

Contents xiii

2.12.8 STAP data collection programs 654

2.12.8.1 Multichannel Airborne Radar Measurements (MCARM) 654

2.12.8.2 Naval Research Laboratory (NRL) database 656

2.12.8.3 Mountaintop database 656

2.12.8.4 Knowledge-aided sensor signal processing and expert reasoning

(KASSPER) data 657

2.12.9 Summary 658

References 661

CHAPTER 13 MIMO Radar with Widely Separated Antennas—From

Concepts to Designs 667

Qian He, Yang Yang and Rick S. Blum


2.13.2 Coherent MIMO radar 669

2.13.2.1 Joint location and velocity estimation 670

2.13.2.2 Coherent ambiguity function 675

2.13.2.3 Localization with phase errors 676

2.13.3 Noncoherent MIMO radar 679

2.13.3.1 Joint location and velocity estimation 680

2.13.3.2 Noncoherent ambiguity function 685

2.13.4 Performance and complexity analysis for coherent and noncoherent

MIMO radar 688

2.13.4.1 Normalized mean square error difference 688

2.13.4.2 Numerical examples 690

2.13.5 Diversity gain for MIMO radar Neyman-Pearson signal detection 692

2.13.5.1 Signal model 692

2.13.5.2 Effect of signal space dimension on diversity gain 693

2.13.6 Phase synchronization for coherent MIMO radar 699

2.13.6.1 Master-slave closed-loop phase synchronization 700

2.13.6.2 Round-trip phase synchronization 701

2.13.6.3 Broadcast consensus based phase synchronization 703

2.13.6.4 Discussion 704

2.13.7 Waveform design for MIMO radar 705

2.13.7.1 Signal model and design criteria 706

2.13.7.2 Optimal waveform design for MIMO radar 709

2.13.7.3 Discussion 711

2.13.8 Summary 712

Appendix 714

A.1 Frequency spread signals 714

A.2 Distinguishing coherent and noncoherent processing 716

xiv Contents

A.3 FIM for coherent MIMO radarjoint estimation 718

A.4 Calculating FIM for localization with phase errors 719

References 723

CHAPTER 14 Optimal Radar Waveform Design 729

Joseph R. Guerci


2.14.2 Optimum transmit-receiver design for the additive colored

noise case: detection 730

2.14.2.1 Application to broadband multipath interference 734

2.14.3 Optimum transmit-receiver design for the clutter case: detection 737

2.14.3.1 Application: sidelobe clutter discrete suppression 739

2.14.3.2 Application: optimum pulse shaping for maximizing SCR 740

2.14.4 Optimizing the transmit-receive functions for target identification 742

2.14.4.1 Application: two-target identification example 744

2.14.4.2 Multi-target identification case 745

2.14.4.3 Application: multitarget ID 746

2.14.5 Constrained optimum transmit-receiver radar 747

2.14.5.1 Case 1: Linear constraints 747

2.14.5.2 Case 2: Nonlinear constraints 750

2.14.6 Open issues and problems 753

2.14.7 Conclusions and future trends 755

Glossary 755

References 757

CHAPTER 15 Multitarget Multisensor Tracking 759

X. Chen, R. Tharmarasa and T. Kirubarajan


2.15.2 Formulation of multisensor-multitarget tracking problems 761

2.15.2.1 Target dynamic models 762

2.15.2.2 Sensor models 763

2.15.2.3 A clutter model 764

2.15.2.4 Spatial clutter intensity estimation 765

2.15.3 Filters 766

2.15.3.1 Kalman filter 767

2.15.3.2 Extended Kalman filter (EKF) 767

2.15.3.3 Unscented Kalman filter (UKF) 768

2.15.3.4 Particle filter 769

2.15.3.5 Interacting multiple-model estimator 770

Contents xv

2.15.4 Filter initialization 773

2.15.4.1 Single-point track initialization 773

2.15.4.2 Two-point difference track initialization 773

2.15.4.3 Issues related to track initialization 773

2.15.5 Data association 774

2.15.6 Multitarget tracking algorithms 778

2.15.6.1 Probabilistic data association (PDA) and joint probabilisticdata association (JPDA) 778

2.15.6.2 Multiple Hypothesis Tracker (MHT) 779

2.15.6.3 Probability hypothesis density (PHD) method 782

2.15.7 Architectures of multisensor-multitarget tracking 783

2.15.8 Centralized tracking 786

2.15.8.1 Sequential updating 786

2.15.8.2 Parallel updating 787

2.15.9 Distributed tracking 788

2.15.9.1 Cross-covariance of the estimation errors 788

2.15.9.2 Association for tracks with dependent errors 789

2.15.9.3 Tracklet fusion 791

2.15.9.4 Covariance intersection 793

2.15.10 Performance evaluation 794

2.15.10.1 Posterior Cramer-Rao Lower Bound (PCRLB) 794

2.15.10.2 Tracker-related measures for performance evaluation 798

2.15.11 Simulations—a multiple closely-spaced target scenario 799

2.15.12 Summary 806

References 806

CHAPTER 16 Passive Bistatic Radar 813

Hugh Griffiths


2.16.2 Bistatic radar 816

2.16.3 Bistatic radar equation 817

2.16.4 Target signatures 818

2.16.4.1 Forward scatter 818

2.16.5 Bistatic radar clutter 821

2.16.6 PBR waveforms 823

2.16.7 The signal and interference environment 827

2.16.7.1 Suppression techniques 830

2.16.7.2 Performance prediction 831

2.16.7.3 Multilateration and tracking 834

xvi Contents

2.16.8 Applications 836

2.16.8.1 Low-cost scientific measurements 836

2.16.8.2 Border or perimeter surveillance 838

2.16.8.3 Air surveillance 839

2.16.8.4 Airborne PBR 839

2.16.8.5 PBR imaging 839

2.16.8.6 Short-range surveillance using WiFi and WiMAX 840

2.16.8.7 PBR using HF skywave signals 846


2.16.9.1 Identifying applications 847

2.16.9.2 Detection of and countermeasures against PBR 847

2.16.9.3 Characterization of bistatic radar clutter 848

2.16.10 Conclusions and future trends 848

Glosssary 851

Acknowledgments 851

References 851

CHAPTER 17 Through-the-Wall Radar Imaging: Theory and Applications 857

Moeness G. Amin and Fauzia Ahmad


2.17.2 Wall clutter mitigation 861

2.17.2.1 Spatial filtering 862

2.17.2.2 Wall parameter estimation, modeling, and subtraction 871

2.17.3 Multipath exploitation 874

2.17.3.1 Image formation algorithm 876

2.17.3.2 Multipath model 877

2.17.3.3 Multipath exploitation algorithm 885

2.17.3.4 Experimental results 886

2.17.4 Change detection based MTI approach 888


2.17.4.2 Change detection 891


2.17.5 Compressive sensing approach to moving target indication for

urban sensing 896


2.17.5.2 Compressive sensing for MTI 898


2.17.6 Applications 901


2.17.8 Data sets 903

Contents xvii

2.17.9 Conclusions 903

References 904

CHAPTER 18 Multi-Channel SAR for Ground Moving Target Indication 911

Stefan V. Baumgartner and Gerhard Krieger


2.18.2 Synthetic aperture radar principle 918

2.18.2.1 SAR acquisition geometry and operation 918

2.18.2.2 Stationary point target signal model 921

2.18.2.3 Pulse compression and image formation 923

2.18.3 Moving point target signal model 925

2.18.3.1 Single-channel signal model 926

2.18.3.2 Multi-channel signal model 929

2.18.4 Effects on SAR imagery 931

2.18.4.1 Residual range cell migration 931

2.18.4.2 Along-track velocity 934

2.18.4.3 Across-track velocity 936

2.18.4.4 Along-track acceleration 940

2.18.4.5 Across-track acceleration 941

2.18.4.6 Summary of effects 941

2.18.5 Classical dual-channel techniques 943

2.18.5.1 Along-track interferometry 943

2.18.5.2 Displaced phase center antenna technique 948

2.18.6 General GMTI processing chain 950

2.18.6.1 Preprocessing 951

2.18.6.2 Clutter suppression 952

2.18.6.3 Detection 953

2.18.6.4 Signal extraction 955

2.18.6.5 Parameter estimation 957

2.18.6.6 Visualization 962

2.18.7 Doppler parameter estimation: basic methods 962

2.18.7.1 Matched filter bank 963

2.18.7.2 Fractional Fourier transform 966

2.18.7.3 Time-frequency analysis 969

2.18.7.4 Radon transform 971

2.18.8 Space-time adaptive Processing 971

2.18.8.1 Joint domain STAP 972

2.18.8.2 Post-doppler STAP 974

2.18.8.3 EDPCA 977

2.18.8.4 ISTAP 978

xviii Contents

2.18.9 Conclusion and future trends 979

2.18.9.1 Summary of algorithms 982

Glossary 982

References 983

CHAPTER 19 Introduction to Inverse Synthetic Aperture Radar 987

Marco Martorella


2.19.2 Historical overview 987

2.19.3 High resolution radar and radar imaging 988

2.19.3.1 Resolution 989

2.19.3.2 High range resolution 990

2.19.3.3 High cross-range resolution 992

2.19.4 Inverse synthetic aperture radar 993

2.19.4.1 FromSARto ISAR 993

2.19.4.2 Geometry 995

2.19.4.3 Signal modeling 996

2.19.4.4 Image formation 998

2.19.5 ISAR image evaluation 1001

2.19.5.1 ISAR image coordinates 1001

2.19.5.2 Additional considerations on the image projection plane and

limitations of the use of the RD technique 1002

2.19.5.3 Point spread function 1002

2.19.5.4 ISAR image resolution 1002

2.19.6 Examples of ISAR images 1004

2.19.7 Image autofocus 1007

2.19.7.1 ICBA 1007

2.19.7.2 IEBA 1009

2.19.7.3 DSA 1009

2.19.7.4 PGA 1010

2.19.8 Time-windowing 1013

2.19.9 Image scaling 1015

2.19.10 Time-frequency image formation 1019

2.19.11 Polarimetric ISAR (Pol-ISAR) 1021

2.19.11.1 Polarimetric SAR imaging 1022

2.19.11.2 Polarimetric ISAR imaging 1023

2.19.12 Bistatic radar imaging 1030

2.19.12.1 Bistatic ISAR 1031


Contents xix

Acknowledgment 1039

References 1040

CHAPTER 20 SAR Interferometry and Tomography: Theory and

Applications 1043

Gianfranco Fornaro and Vito Pascazio


2.20.1.1 Microwave high resolution imaging 1043

2.20.2 Basics concepts in SAR imaging and SAR interferometry 1046

2.20.2.1 High resolution image formation 1046

2.20.2.2 Operational modes 1050

2.20.3 SAR interferometry 1053

2.20.3.1 Across-track SAR interferometry for measuring the surface

topography 1054

2.20.3.2 Statistical characterization of across-track SAR interferometric

signals 1055

2.20.3.3 The differential SAR interferometry technique for measuring

displacements 1061

2.20.3.4 Phase unwrapping 1066

2.20.4 Multibaseline SAR interferometry 1071

2.20.4.1 Bayesian statistical solution to PhU problem 1076

2.20.4.2 Graph cuts solution to PhU problem 1078

2.20.5 Multipass interferometry 1080

2.20.5.1 Multipass phase unwrapping 1088

2.20.6 SAR tomography 1093

2.20.6.1 Linear non-adaptive inversion for SAR tomography 1095

2.20.6.2 Linear adaptive inversion for SAR tomography 1096

2.20.6.3 Compressive sensing inversion for SAR tomography 1098

2.20.6.4 Performance comparison of SAR tomography methods 1101

2.20.7 4D Imaging 1103


Acknowledgments 1111

References 1111

CHAPTER 21 Radar Polarimetry Basics and Selected Earth Remote

Sensing Applications 1119

Laurent Ferro-Famil and Eric Pottier


2.21.1.1 Historical overview of radar polarimetry 1119

xx Contents

2.21.1.2 Organization of the work 1120

2.21.2 SAR polarimetry basics 1121

2.21.2.1 Polarimetric scattering: formalism, descriptors, and operators 1121

2.21.2.2 Polarimetric decomposition techniques 1142

2.21.2.3 A simple polarimetric classification scheme 1150

2.21.3 Advanced polarimetric decomposition techniques and some of their

applications 1162

2.21.3.1 Advanced decomposition techniques 1162

2.21.3.2 Advanced characterization of scattering mechanisms 1175

2.21.3.3 Characterization of soils using polarimetric decomposition

techniques 1181

2.21.3.4 A methods using H, A, and a and the SPM model 1182

2.21.3.5 A method using reflection symmetry and the IEM model 1183

2.21.4 Selected topics in multidimensional polarimetric SAR signal processing 1187

2.21.4.1 Coherent Time-Frequency characterization of complex

polarimetric features 1187

2.21.4.2 Analysis of volumetric media using polarimetric SAR

interferometry 1212

2.21.4.3 Analysis of volumetric media using polarimetric SAR

tomography 1222

2.21.5 Conclusion and trends 1240

References 1241

CHAPTER 22 Integrated Sensor Systems and Data Fusion for Homeland

Protection 1245

Alfonso Farina, Luciana Ortenzi, Branko Ristic and Alex Skvortsor


2.22.2 The problem of homeland protection 1249

2.22.3 Definitions and background 1252

2.22.4 The information sources 1258

2.22.5 Homogeneous sensor networks 1259

2.22.5.1 Sensor configuration 1260

2.22.5.2 Classical approach to surveillance 1261

2.22.5.3 Collaborative signal and information processing (CSIP) 1263

2.22.5.4 Self-Organizing Sensor Networks 1264

2.22.5.5 Self-synchronization mechanism applied to sensor network 1265

2.22.5.6 From theory to real application problems 1265

2.22.5.7 Rethinking mathematical algorithms for net-centric approaches 1266

2.22.6 Real study cases: novel approaches to sensor networks 1268

2.22.6.1 A cooperative sensor network: optimal deployment and functioning.... 1268

Contents xxi

2.22.6.2 Modeling and performance analysis of a network of chemical

sensors with dynamic collaboration 1275

2.22.6.3 Detection and localization of radioactive point sources with

experimental verification 1279

2.22.7 Heterogeneous multi-sensor network management 1285

2.22.7.1 Sensor deployment 1285

2.22.7.2 Sensor behavior assignment 1286

2.22.7.3 Sensor coordination in a decentralized sensor network 1287

2.22.7.4 A mathematical issue for multi-sensor networks 1288

2.22.8 Border control problem via electronic fence 1290

2.22.8.1 Multi-scale approach 1290

2.22.8.2 The electronic fence 1291

2.22.9 Estimation and forecasting of an epidemic 1302

2.22.9.1 Modeling 1304

2.22.9.2 Sequential Bayesian solution 1305

2.22.9.3 Numerical results 1307


Acknowledgments 1311

References 1311

Index 1321

Academic Press Library Signal Processing - gbv.de · Dipartimento diIngegneria dell'lnformazione,...

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Transcript of Academic Press Library Signal Processing - gbv.de · Dipartimento diIngegneria dell'lnformazione,...