Modern Radar Detection Theory

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 processin...

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Bibliographic Details
Main Authors De Maio, Antonio, Greco, Maria Sabrina
Format eBook
LanguageEnglish
Published Edison The Institution of Engineering and Technology 2016
Institution of Engineering and Technology (The IET)
Institution of Engineering & Technology
SciTech Publishing
Edition1
SeriesElectromagnetics and Radar
Subjects
Aerospace & Radar Technology
Radar
Radar & Navigation Systems
Radar defense networks
Signal processing
TECHNOLOGY & ENGINEERING
radar detection
radar antennas
radar clutter
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Table of Contents:
  • Chapter 1: Introduction to Radar Detection -- Chapter 2: Radar Detection inWhite Gaussian Noise: A GLRT Framework -- Chapter 3: Subspace Detection for Adaptive Radar: Detectors and Performance Analysis -- Chapter 4: Two-Stage Detectors for Point-Like Targets in Gaussian Interference with Unknown Spectral Properties -- Chapter 5: Bayesian Radar Detection in Interference -- Chapter 6: Adaptive Radar Detection for Sample-Starved Gaussian Training Conditions -- Chapter 7: Compound-Gaussian Models and Target Detection: A Unified View -- Chapter 8: Covariance Matrix Estimation in SIRV and Elliptical Processes and Their Applications in Radar Detection -- Chapter 9: Detection of Extended Target in Compound-Gaussian Clutter
  • Title Page Table of Contents 1. Introduction to Radar Detection 2. Radar Detection in White Gaussian Noise: A GLRT Framework 3. Subspace Detection for Adaptive Radar: Detectors and Performance Analysis 4. Two-Stage Detectors for Point-Like Targets in Gaussian Interference with Unknown Spectral Properties 5. Bayesian Radar Detection in Interference 6. Adaptive Radar Detection for Sample-Starved Gaussian Training Conditions 7. Compound-Gaussian Models and Target Detection: A Unified View 8. Covariance Matrix Estimation in SIRV and Elliptical Processes and Their Applications in Radar Detection 9. Detection of Extended Target in Compound-Gaussian Clutter Index
  • Intro -- Contents -- 1. Introduction to Radar Detection - Antonio De Maio, Maria S. Greco, and Danilo Orlando -- 1.1. Historical Background and Terminology -- 1.2. Symbols -- 1.3. Detection Theory -- 1.4. Organization, Use, and Outline of the Book -- 1.5. References -- References -- 2. Radar Detection in White Gaussian Noise: A GLRT Framework - Ernesto Conte, Antonio De Maio, and Guolong Cui -- 2.1. Introduction -- 2.2. Problem Formulation -- 2.3. Reduction by Sufficiency -- 2.4. Optimum NP Detector and Existence of the UMP Test -- 2.5. GLRT Design -- 2.6. Performance Analysis -- 2.7. Conclusions and Further Reading -- References -- 3. Subspace Detection for Adaptive Radar: Detectors and Performance Analysis - Ram S. Raghavan, Shawn Kraut, and Christ D. Richmond -- 3.1. Introduction -- 3.2. Introduction to Signal Detection in Interference and Noise -- 3.3. Subspace Signal Model and Invariant Hypothesis Tests -- 3.4. Analytical Expressions for PsubD and PsubFA -- 3.5. Performance Results of Adaptive Subspace Detectors -- 3.6. Summary and Conclusions -- Appendix 3.A -- Appendix 3.B -- Appendix 3.C -- Appendix 3.D -- References -- 4. Two-Stage Detectors for Point-Like Targets in Gaussian Interference with Unknown Spectral Properties - Antonio De Maio, Chengpeng Hao, and Danilo Orlando -- 4.1. Introduction: Principles of Design -- 4.2. Two-Stage Architecture Description, Performance Analysis, and Comparisons -- 4.3. Conclusions -- References -- 5. Bayesian Radar Detection in Interference - Pu Wang, Hongbin Li, and Braham Himed -- 5.1. Introduction -- 5.2. General STAP Signal Model -- 5.3. KA-STAP Models -- 5.4. Knowledge-Aided Two-Layered STAP Model -- 5.5. Knowledge-Aided Parametric STAP Model -- 5.6. Summary -- Appendix 5.A -- Appendix 5.B -- References
  • 6. Adaptive Radar Detection for Sample-Starved Gaussian Training Conditions - Yuri I. Abramovich and Ben A. Johnson -- 6.1. Introduction -- 6.2. Improving Adaptive Detection Using EL-Selected Loading -- 6.3. Improving Adaptive Detection Using Covariance Matrix Structure -- 6.4. Improving Adaptive Detection Using Data Partitioning -- References -- 7. Compound-Gaussian Models and Target Detection: A Unified View - K. James Sangston, Maria S. Greco, and Fulvio Gini -- 7.1. Introduction -- 7.2. Compound-Exponential Model for Univariate Intensity -- 7.3. Role of Number Fluctuations -- 7.4. Complex Compound-Gaussian Random Vector -- 7.5. Optimum Detection of a Signal in Complex Compound-Gaussian Clutter -- 7.6. Suboptimum Detectors in Complex Compound-Gaussian Clutter -- 7.7. New Interpretation of the Optimum Detector -- Appendix 7.A -- References -- 8. Covariance Matrix Estimation in SIRV and Elliptical Processes and Their Applications in Radar Detection- Jean-Philippe Ovarlez, Frédéric Pascal, and Philippe Forster -- 8.1. Background and Problem Statement -- 8.2. Non-Gaussian Environment Modeling -- 8.3. Covariance Matrix Estimation in CES Noise -- 8.4. Optimal Detection in CES Noise -- 8.5. Persymmetric Structured Covariance Matrix Estimation -- 8.6. Radar Applications -- 8.7. Conclusion -- References -- 9. Detection of Extended Target in Compound-Gaussian Clutter - Augusto Aubry, Javier Carretero-Moya, Antonio De Maio, Antonio Pauciullo, Javier Gismero-Menoyo, and Alberto Asensio-Lopez -- 9.1. Introduction -- 9.2. Distributed Target Coherent Detection -- 9.3. High-Resolution Experimental Data -- 9.4. Experimental CFAR Behavior -- 9.5. Detection Performance -- 9.6. Conclusions -- Appendix 9.A -- References -- Index