Adaptive Detection for Multichannel Signals in Non-Ideal Environments

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Table of Contents
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Chapter 1  Overview of Adaptive Detection
	1.1 INTRODUCTION
	1.2 BLOCK DIAGRAM AND SIGNAL MODEL
		1.2.1 Block Diagram for Adaptive Detection
		1.2.2 Multichannel Signal Model
	1.3 DESIGN CRITERIA OF ADAPTIVE DETECTORS
		1.3.1 The Rao Test
		1.3.2 The Wald Test
		1.3.3 The Gradient Test
		1.3.4 The Generalized Likelihood Ratio Test
	1.4 TYPES OF NON-IDEAL ENVIRONMENTS
		1.4.1 The Sample-Starved Scenarios
		1.4.2 The Signal Mismatch Scenarios
		1.4.3 The Presence of Interference
	1.5 ORGANIZATION AND OUTLINE OF THE BOOK
	REFERENCES
Chapter 2 Adaptive  Detectors in Sample-Starved Environments
	2.1 ADAPTIVE POINT-LIKE TARGET DETECTION IN AUTOREGRESSIVE NOISE
		2.1.1 Detector Design
		2.1.2 The Parameter Estimation of the Autoregressive Process
		2.1.3 Numerical Examples
	2.2 ADAPTIVE DISTRIBUTED TARGET DETECTION IN AUTOREGRESSIVE NOISE
		2.2.1 Detector Design
		2.2.2 Fisher Information Matrix
		2.2.3 The Maximum Likelihood Estimates of the Unknown Parameters
		2.2.4 Asymptotic Performance Analysis
		2.2.5 Numerical Examples
	2.3 ADAPTIVE DETECTOR BASED ON ORTHOGONAL PARTITION OF THE DATA
		2.3.1 Problem Formulation
		2.3.2 Detector Design
		2.3.3 Performance Assessment
		2.3.4 Numerical Examples
	2.4 ADAPTIVE DISTRIBUTED TARGET DETECTION IN THE PARTIALLY HOMOGENEOUS NOISE WITH PERSYMMETRIC STRUCTURE
		2.4.1 Problem Formulation
		2.4.2 The Rao Test
		2.4.3 The Wald Test
		2.4.4 Experimental Results
	2.5 ADAPTIVE SUBSPACE SIGNAL DETECTION IN THE PARTIALLY HOMOGENEOUS NOISE WITH PERSYMMETRIC STRUCTURE
		2.5.1 Problem Formulation
		2.5.2 The One-Step Generalized Likelihood Ratio Test
		2.5.3 The Rao Test
		2.5.4 The Wald Test
		2.5.5 CFAR Property Analysis
		2.5.6 Experimental Results
	2.6 CONCLUSION
	APPENDIX 2.A THE CIRCULARLY SYMMETRIC COMPLEX GAUSSIAN VECTOR
	APPENDIX 2.B THE DERIVATION OF (2.87)
	NOTE
	REFERENCES
Chapter 3 Adaptive Selective Detectors for Mismatched Signals in the Presence of Signal Mismatch
	3.1 ADAPTIVE SELECTIVE DETECTORS IN PARTIALLY HOMOGENEOUS ENVIRONMENT
		3.1.1 Problem Formulation
		3.1.2 One-Step Selective Detector
		3.1.3 Two-Step Selective Detector
		3.1.4 Numerical Examples
	3.2 ADAPTIVE SELECTIVE DETECTORS IN THE COMPOUND GAUSSIAN ENVIRONMENT
		3.2.1 Problem Formulation
		3.2.2 Two-Step Generalized Likelihood Ratio Test
		3.2.3 Maximum A Posteriori Generalized Likelihood Ratio Test
		3.2.4 Numerical Examples
	3.3 CONCLUSION
	APPENDIX 3.A THE CFAR PROPERTY ANALYSIS
	REFERENCES
Chapter 4 Adaptive Robust Detector in the Presence of Signal Mismatch
	4.1 PROBLEM FORMULATION
	4.2 ADAPTIVE ROBUST GRADIENT DETECTOR
	4.3 NUMERICAL EXAMPLES
		4.3.1 Mismatched Detection Performance Analysis
		4.3.2 Matched Detection Performance Analysis
	4.4 CONCLUSION
	APPENDIX 4.A THE CFAR PROPERTY ANALYSIS
	REFERENCES
Chapter 5 Tunable Adaptive Detector for Mismatched Signals
	INTRODUCTION
	5.2 PROBLEM FORMULATION AND DETECTOR DESIGN
	PERFORMANCE EVALUATIONWe
	5.4 NUMERICAL EXAMPLES
	5.5 CONCLUSION
	NOTE
	REFERENCES
Chapter 6 Adaptive Detection of a Subspace Signal in Interference
	6.1 ADAPTIVE DETECTORS IN SIGNAL-DEPENDENT INTERFERENCE
		6.1.1 Problem Formulation
		6.1.2 The Rao Test
		6.1.3 The Wald Test
		6.1.4 The Generalized Likelihood Ratio Test
		6.1.5 Analytical Performance
		6.1.6 Numerical Examples
	6.2 ADAPTIVE DETECTORS IN SUBSPACE INTERFERENCE PLUS HOMOGENEOUS CLUTTER
		6.2.1 Problem Formulation
		6.2.2 Wald Test-Based Detectors
		6.2.3 Numerical Examples
	6.3 ADAPTIVE DETECTORS IN SUBSPACE INTERFERENCE PLUS PARTIALLY HOMOGENEOUS CLUTTER
		6.3.1 Design of the Detector
		6.3.2 Numerical Examples
	6.4 CONCLUSION
	APPENDIX 6.A THE DERIVATION OF EQUATIONS 6.147 AND 6.148
	NOTES
	REFERENCES
Chapter 7 Future Trends
	REFERENCES