Saturday, October 1, 2016

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


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