Advances in Adaptive Radar Detection and Range Estimation

✍ Scribed by Chengpeng Hao, Danilo Orlando, Jun Liu, Chaoran Yin

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 226
• Edition: 1
• Category: Library

✦ Synopsis

This book provides a comprehensive and systematic framework for the design of adaptive architectures, which take advantage of the available a priori information to enhance the detection performance. Moreover, this framework also provides guidelines to develop decision schemes capable of estimating the target position within the range bin. To this end, the readers are driven step-by-step towards those aspects that have to be accounted for at the design stage, starting from the exploitation of system and/or environment information up to the use of target energy leakage (energy spillover), which allows inferring on the target position within the range cell under test.In addition to design issues, this book presents an extensive number of illustrative examples based upon both simulated and real-recorded data. Moreover, the performance analysis is enriched by considerations about the trade-off between performances and computational requirements.Finally, this book could be a valuable resource for PhD students, researchers, professors, and, more generally, engineers working on statistical signal processing and its applications to radar systems.

✦ Table of Contents

Acronyms and Symbols
Contents
1 Introduction to Radar Systems
1.1 Historical Background
1.2 Pulsed Radar Architectures
1.3 An Introduction to Design Parameters
1.3.1 The Ambiguity Function
1.3.2 Doppler Resolution and the Pulse Burst Waveform
1.3.3 Radar Equation
1.4 Organization and Outline of the Book
References
2 Adaptive Radar Detection: Classical Approach
2.1 Analytical Models for Target and Interference
2.2 Decision Theory in Radar
2.2.1 Hypothesis Testing Problems
2.2.2 Design Criteria
2.3 Conventional Detectors for Point-Like Targets
2.3.1 Decision Rules
2.3.2 CFAR Property
References
3 Knowledge-Aided Detectors
3.1 Persymmetric Detectors
3.1.1 Problem Formulation
3.1.2 Detector Designs
3.1.3 Illustrative Examples
3.2 Symmetric Spectrum Detectors
3.2.1 Problem Formulation
3.2.2 Detector Designs
3.2.3 Illustrative Examples
3.3 Joint Exploitation of Persymmetry and Symmetry
3.3.1 Problem Formulation
3.3.2 Detector Designs
3.3.3 Illustrative Examples
References
4 Detectors with Enhanced Range Estimation Capabilities
4.1 Localization Detectors for Point-Like Targets
4.1.1 Problem Formulation
4.1.2 Detector Designs
4.1.3 Illustrative Examples
4.2 Polarimetric Localization Detectors
4.2.1 Problem Formulation
4.2.2 Detector Designs
4.2.3 Illustrative Examples
4.3 Oversampling Localization Detectors
4.3.1 Problem Formulation
4.3.2 Detector Designs
4.3.3 Illustrative Examples
References
5 Knowledge-Aided Localization Detectors
5.1 Persymmetric Localization Detectors
5.1.1 Problem Formulation
5.1.2 Detector Designs
5.1.3 Illustrative Examples
5.2 Symmetric Spectrum Localization Detectors
5.2.1 Problem Formulation
5.2.2 Detector Designs
5.2.3 Illustrative Examples
5.3 Bayesian Localization Detectors
5.3.1 Problem Formulation
5.3.2 Detector Designs
5.3.3 Illustrative Examples
References
Appendix A Complex Gaussian Distribution with Circular Symmetry
Appendix B The Equivalent form of Detector (3.25)
Appendix C Derivations of the Distribution of Defined in (3.33)
Appendix D The Equivalent form of Detector (3.61)
Appendix E The Proof of Proposition 3.2
Appendix F The Proof of Proposition 3.4
Appendix G Expressions of the Coefficients for (3.128) and (3.130)
Appendix H The Proof of Proposition 3.5
Appendix I The Correlation Model of the Clutter Returns
References