Modern radar detection theory / edited by Antonio de Maio, Dipartimento di Ingegneria Elettrica e delle Tecnologie dell'Informazione, Università degli Studi di Napoli "Federico II", Maria Sabrina Greco, Dipartimento di Ingegneria dell'Informazione, Università di Pisa.

Format:

Book

Contributor:

De Maio, Antonio, editor.

Greco, Maria Sabrina, editor.

Language:

English

Subjects (All):

Radar.

Signal theory (Telecommunication).

Physical Description:

1 online resource (395 p.)

Place of Publication:

Edison, New Jersey : SciTech Publishing, and imprint of the IET, [2016]

Language Note:

English

Summary:

Modern radar detection is the new frontier for advanced radar systems capable of operating in challenging scenarios with a plurality of interference sources, both manmade and natural. Written by top researchers and recognized leaders in the field, this is the first book to provide a comprehensive understanding of the current research trends in modern radar detection. It updates readers with the latest radar signal processing algorithms now capable with high-speed computer chips and sophisticated programs. It also includes examples and applications from real systems. This is essential reading for radar systems design engineers within aerospace companies, military radar engineers, and aerospace contractors/consultants.

Contents:

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 Orlando4.1. 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

References6. 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 Vector7.5. 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

Notes:

Description based upon print version of record.

Includes bibliographical references and index.

Description based on online resource; title from PDF title page (ebrary, viewed March 23, 2016).

ISBN:

1-5231-0175-X

1-61353-200-8