Radar Detection TOC

spendergast: Modern radar detection theory / edited by Antonio De Maio, Maria Sabrina Greco

Contents

1 Introduction to Radar Detection 1

• Antonio De Maio, Maria S. Greco, and Danilo Orlando
• 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 21

• Ernesto Conte, Antonio De Maio, and Guolong Cui
• 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 43

• Ram S. Raghavan, Shawn Kraut, and Christ D. Richmond
• 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 PD and PFA 54
• 3.4.1 PD and PFA for Subspace GLRT 54
• 3.4.2 PD and PFA for Subspace AMF Test 55
• 3.4.3 PD and PFA 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 GaussianInterference with Unknown Spectral Properties 85

• Antonio De Maio, Chengpeng Hao, and Danilo Orlando
• 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 133

• Pu Wang, Hongbin Li, and Braham Himed
• 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 165

• Yuri I. Abramovich and Ben A. Johnson
• 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 263

• K. James Sangston, Maria S. Greco, and Fulvio Gini
• 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 295

• Jean-Philippe Ovarlez, Frédéric Pascal, and Philippe Forster
• 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 
• 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 302
• 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 308
• 8.4 Optimal Detection in CES Noise 312
• 8.5 Persymmetric Structured Covariance Matrix Estimation 313
• 8.5.1 Detection in Circular Gaussian Noise 314
• 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
• 8.7 Conclusion 323
• References 327

9 Detection of Extended Target in Compound-Gaussian Clutter 333

• Augusto Aubry, Javier Carretero-Moya, Antonio De Maio, Antonio Pauciullo, Javier Gismero-Menoyo, and Alberto Asensio-Lopez
• 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 Conclusions Appendix 
• 9.A References

Index 359