Modern Radar Detection Theory

by Antonio De Maio (Editor), Maria Sabrina Greco (Editor)

Overview

Recently, various algorithms for radar signal detection that rely heavily upon complicated processing and/or antenna architectures have been the subject of much interest. These techniques owe their genesis to several factors. One is revolutionary technological advances in high-speed signal processing hardware and digital array radar technology. Another is the stress on requirements often imposed by defence applications in areas such as airborne early warning and homeland security.

This book explores these emerging research thrusts in radar detection with advanced radar systems capable of operating in challenging scenarios with a plurality of interference sources, both man-made and natural. Topics covered include: adaptive radar detection in Gaussian interference with unknown spectral properties; invariance theory as an instrument to force the Constant False Alarm Rate (CFAR) property at the design stage; one- and two-stage detectors and their performances; operating scenarios where a small number of training data for spectral estimation is available; Bayesian radar detection to account for prior information in the interference covariance matrix; and radar detection in the presence of non-Gaussian interference. Detector design techniques based on a variety of criteria are thoroughly presented and CFAR issues are discussed. Performance analyses representative of practical airborne, as well as ground-based and shipborne, radar situations are shown.

Results on real radar data are also discussed. Modern Radar Detection Theory provides a comprehensive reference on the latest developments in adaptive radar detection for researchers, advanced students and engineers working on statistical signal processing and its applications to radar systems.

Product Details

ISBN-13:	9781613531990
Publisher:	The Institution of Engineering and Technology
Publication date:	11/25/2015
Series:	Radar, Sonar and Navigation
Pages:	400
Product dimensions:	7.30(w) x 10.20(h) x 1.00(d)

About the Author

Antonio De Maio is a Professor at the Universityof Naples Federico II, Department of Electrical Engineering and Information Technology, where his research interests lie in the field of statistical signal processing, with emphasis on radar detection and convex optimization applied to radar signal processing. He is a Senior Area Editor of IEEE Transactions on Signal Processing, a member of the IEEE AESS Radar Panel and of the IEEE Sensor Array Processing Technical Committee, and has published more than 200 technical papers in international journals or proceedings of international conferences.

Maria Sabrina Greco is a Professor at the Universityof Pisa, Department of Information Engineering, where her research interests include radar clutter models, coherent and incoherent detection in non-Gaussian clutter, passive radars, multistatic and cognitive radars. She serves on the editorial boards of IET Radar, Sonar and Navigation and Journal of Advances in Signal Processing, and she is the Editor-in-Chief of the IEEE Aerospace and Electronic Systems Magazine. She is a member of the IEEE Sensor Array Processing Technical Committees, of the IEEE AESS and of SP Boards of Governors, and Chair of the IEEE AESS Radar Panel.

Table of Contents

1 Introduction to Radar Detection Antonio De Maio Maria S. Greco Danilo Orlando 1

1.1 Historical Background and Terminology 1

1.2 Symbols 5

1.3 Detection Theory 6

1.3.1 Signal and Interference Models 7

1.3.2 Basic Concepts 9

1.3.3 Detector Design Criteria 11

1.3.4 CFAR Property and Invariance in Detection Theory 13

1.4 Organization, Use, and Outline of the Book 14

1.5 References 16

References 17

2 Radar Detection in White Gaussian Noise: A GLRT Framework Ernesto Conte Antonio De Maio Guolong Cui 21

2.1 Introduction 21

2.2 Problem Formulation 22

2.3 Reduction by Sufficiency 24

2.4 Optimum NP Detector and Existence of the UMP Test 26

2.4.1 Coherent Case 26

2.4.2 Non-coherent Case 27

2.5 GLRT Design 28

2.6 Performance Analysis 32

2.6.1 Coherent Case 32

2.6.2 Non-coherent Case 36

2.7 Conclusions and Further Reading 40

References 41

3 Subspace Detection for Adaptive Radar: Detectors and Performance Analysis Ram S. Raghavan Shawn Kraut Christ D. Richmond 43

3.1 Introduction 43

3.2 Introduction to Signal Detection in Interference and Noise 45

3.2.1 Detecting a Known Signal in Colored Gaussian Noise 46

3.2.2 Detecting a Known Signal with Unknown Phase in Zero-Mean Colored Gaussian Noise 47

3.3 Subspace Signal Model and Invariant Hypothesis Tests 48

3.3.1 Subspace Signal Model 49

3.3.2 A Rationale for Subspace Signal Model 49

3.3.3 Hypothesis Test 51

3.3.4 Maximum Invariants for Subspace Signal Detection in Interference and Noise 53

3.4 Analytical Expressions for P_D and P_FA 54

3.4.1 P_D and P_FA for Subspace GLRT 54

3.4.2 P_D and P_FA for Subspace AMF Test 55

3.4.3 P_D and P_FA for Subspace ACE Test 56

3.5 Performance Results of Adaptive Subspace Detectors 57

3.6 Summary and Conclusions 70

Appendix 3.A 71

Appendix 3.B 74

Appendix 3.C 75

Appendix 3-D 79

References 80

4 Two-Stage Detectors for Point-Like Targets in Gaussian Interference with Unknown Spectral Properties Antonio De Maio Chengpeng Hao Danilo Orlando 85

4.1 Introduction: Principles of Design 85

4.2 Two-Stage Architecture Description, Performance Analysis, and Comparisons 91

4.2.1 The Adaptive Sidelobe Blanker 93

4.2.2 Modifications of the ASB towards Robustness: The Subspace-Based ASB 97

4.2.3 Modifications of the ASB towards Selectivity 104

4.2.4 Modifications of the ASB towards both Selectivity and Robustness 117

4.2.5 Selective Two-Stage Detectors 125

4.3 Conclusions 128

References 129

5 Bayesian Radar Detection in Interference Pu Wang Hongbin Li Braham Himed 133

5.1 Introduction 133

5.2 General STAP Signal Model 134

5.3 KA-STAP Models 136

5.3.1 Knowledge-Aided Homogeneous Model 136

5.3.2 Bayesian GLRT (B-GLRT) and Bayesian AMF (B-AMF) 138

5.3.3 Selection of Hyperparameter 140

5.3.4 Extensions to Partially Homogeneous and Compound-Gaussian Models 144

5.4 Knowledge-Aided Two-Layered STAP Model 147

5.5 Knowledge-Aided Parametric STAP Model 151

5.6 Summary 159

Appendix 5.A 159

Appendix 5.B 160

References 162

6 Adaptive Radar Detection for Sample-Starved Gaussian Training Conditions Yuri I. Abramovich Ben A. Johnson 165

6.1 Introduction 165

6.2 Improving Adaptive Detection Using EL-Selected Loading 168

6.2.1 Single Adaptive Filter Formed with Secondary Data, Followed by Adaptive Thresholding Using Primary Data 168

6.2.2 Different Adaptive Process per Test Cell with Combined Adaptive Filtering and Detection Using Secondary Data 170

6.2.3 Observations 209

6.3 Improving Adaptive Detection Using Covariance Matrix Structure 212

6.3.1 Background: TVAR(m) Approximation of a Hermitian Covariance Matrix, ML Model Identification and Order Estimation 215

6.3.2 Performance Analysis of TVAR(m)-Based Adaptive Filters and Adaptive Detectors for TVAR(m) or AR(m) Interferences 218

6.3.3 Simulation Results of TVAR(m)-Based Adaptive Detectors for TVAR(m) or AR(m) Interferences 223

6.3.4 Observations 237

6.4 Improving Adaptive Detection Using Data Partitioning 239

6.4.1 Analysis Performance of "One-Stage" Adaptive CFAR Detectors versus "Two-Stage" Adaptive Processing 242

6.4.2 Comparative Detection Performance Analysis 247

6.4.3 Observations 255

References 257

7 Compound-Gaussian Models and Target Detection: A Unified View K. James Sangston Maria S. Greco Fulvio Gini 263

7.1 Introduction 263

7.2 Compound-Exponential Model for Univariate Intensity 264

7.2.1 Intensity Tail Distribution and Completely Monotonic Functions 264

7.2.2 Examples 265

7.3 Role of Number Fluctuations 266

7.3.1 Transfer Theorem and the CLT 267

7.3.2 Models for Number Fluctuations 269

7.4 Complex Compound-Gaussian Random Vector 270

7.5 Optimum Detection of a Signal in Complex Compound-Gaussian Clutter 272

7.5.1 Likelihood Ratio and Data-Dependent Threshold Interpretation 273

7.5.2 Likelihood Ratio and the Estimator-Correlator Interpretation 275

7.6 Suboptimum Detectors in Complex Compound-Gaussian Clutter 275

7.6.1 Suboptimum Approximations to Likelihood Ratio 276

7.6.2 Suboptimum Approximations to the Data-Dependent Threshold 277

7.6.3 Suboptimum Approximations to Estimator-Correlator 278

7.6.4 Performance Evaluation of Optimum and Suboptimum Detectors 280

7.7 New Interpretation of the Optimum Detector 281

7.7.1 Product of Estimators Formulation 281

7.7.2 General Properties of Product of Estimators 283

Appendix 7.A 290

References 292

8 Covariance Matrix Estimation in SIRV and Elliptical Processes and Their Applications in Radar Detection Jean-Philippe Ovarlez Frédéric Pascal Philippe Forster 295

8.1 Background and Problem Statement 295

8.1.1 Background Parameter Estimation in Gaussian Case 296

8.1.2 Optimal Detection in Gaussian Case 297

8.2 Non-Gaussian Environment Modeling 300

8.2.1 CES Distribution 300

8.2.2 The Subclass of SIRV 301

8.3 Covariance Matrix Estimation in CES Noise 302

8.3.1 M-Estimators 303

8.3.2 Properties of the M-Estimators 304

8.3.3 Asymptotic Distributions of the M-Estimators 305

8.3.4 Link to M-Estimators in the SIRV Framework 307

8.4 Optimal Detection in CES Noise 312

8.5 Persymmetric Structured Covariance Matrix Estimation 313

8.5.1 Detection in Circular Gaussian Noise 315

8.5.2 Detection in Non-Gaussian Noise 315

8.6 Radar Applications 317

8.6.1 Ground-Based Radar Detection 317

8.6.2 Nostradamus Radar Detection 319

8.6.3 STAP Detection 320

8.6.4 Robustness of the FPE 323

8.7 Conclusion 327

References 327

9 Detection of Extended Target in Compound-Gaussian Clutter Augusta Aubry Javier Carretero-Moya Antonio De Maio Antonio Pauciulio Javier Gismero-Menoyo Alberto Asensio-Lopez 333

9.1 Introduction 333

9.2 Distributed Target Coherent Detection 334

9.2.1 Overview 334

9.2.2 Rank-One Steering 336

9.2.3 Subspace Steering 338

9.2.4 Covariance Estimation 340

9.3 High-Resolution Experimental Data 342

9.3.1 Sea-Clutter Data 343

9.3.2 Maritime Target Data 345

9.4 Experimental CFAR Behavior 350

9.5 Detection Performance 355

9.5.1 Detection Probability: Simulated Target and Real Clutter 355

9.5.2 Detection Maps: Real Target and Clutter Data 358

9.6 Conelusions 359

Appendix 9.A 360

References 367

Index 375