Modern Radar Detection Theory
Published on 25. November 2015
Book
Hardback
400 pages
978-1-61353-199-0 (ISBN)
Description
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 processing hardware and digital array radar technology. Another is the stress on requirements often imposed by defence applications in areas such as airborne early warning and homeland security.
This book explores these emerging research thrusts in radar detection with advanced radar systems capable of operating in challenging scenarios with a plurality of interference sources, both man-made and natural. Topics covered include: adaptive radar detection in Gaussian interference with unknown spectral properties; invariance theory as an instrument to force the Constant False Alarm Rate (CFAR) property at the design stage; one- and two-stage detectors and their performances; operating scenarios where a small number of training data for spectral estimation is available; Bayesian radar detection to account for prior information in the interference covariance matrix; and radar detection in the presence of non-Gaussian interference. Detector design techniques based on a variety of criteria are thoroughly presented and CFAR issues are discussed. Performance analyses representative of practical airborne, as well as ground-based and shipborne, radar situations are shown.
Results on real radar data are also discussed. Modern Radar Detection Theory provides a comprehensive reference on the latest developments in adaptive radar detection for researchers, advanced students and engineers working on statistical signal processing and its applications to radar systems.
This book explores these emerging research thrusts in radar detection with advanced radar systems capable of operating in challenging scenarios with a plurality of interference sources, both man-made and natural. Topics covered include: adaptive radar detection in Gaussian interference with unknown spectral properties; invariance theory as an instrument to force the Constant False Alarm Rate (CFAR) property at the design stage; one- and two-stage detectors and their performances; operating scenarios where a small number of training data for spectral estimation is available; Bayesian radar detection to account for prior information in the interference covariance matrix; and radar detection in the presence of non-Gaussian interference. Detector design techniques based on a variety of criteria are thoroughly presented and CFAR issues are discussed. Performance analyses representative of practical airborne, as well as ground-based and shipborne, radar situations are shown.
Results on real radar data are also discussed. Modern Radar Detection Theory provides a comprehensive reference on the latest developments in adaptive radar detection for researchers, advanced students and engineers working on statistical signal processing and its applications to radar systems.
More details
Series
Language
English
Place of publication
New York
United States
Target group
College/higher education
Professional and scholarly
Product notice
sewn/stitched
Cloth over boards
Dimensions
Height: 260 mm
Width: 183 mm
Thickness: 25 mm
Weight
939 gr
ISBN-13
978-1-61353-199-0 (9781613531990)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Classification
Other editions
Additional editions
Antonio De Maio | Maria Sabrina Greco
Modern Radar Detection Theory
E-Book
12/2015
1st Edition
SciTech Publishing Inc
€373.99
Available for download
Persons
Antonio De Maio is a Professor at the University of Naples Federico II, Department of Electrical Engineering and Information Technology, where his research interests lie in the field of statistical signal processing, with emphasis on radar detection and convex optimization applied to radar signal processing. He is a Senior Area Editor of IEEE Transactions on Signal Processing, a member of the IEEE AESS Radar Panel and of the IEEE Sensor Array Processing Technical Committee, and has published more than 200 technical papers in international journals or proceedings of international conferences.
Maria Sabrina Greco is a Professor at the University of Pisa, Department of Information Engineering, where her research interests include radar clutter models, coherent and incoherent detection in non-Gaussian clutter, passive radars, multistatic and cognitive radars. She serves on the editorial boards of IET Radar, Sonar and Navigation and Journal of Advances in Signal Processing, and she is the Editor-in-Chief of the IEEE Aerospace and Electronic Systems Magazine. She is a member of the IEEE Sensor Array Processing Technical Committees, of the IEEE AESS and of SP Boards of Governors, and Chair of the IEEE AESS Radar Panel.
Maria Sabrina Greco is a Professor at the University of Pisa, Department of Information Engineering, where her research interests include radar clutter models, coherent and incoherent detection in non-Gaussian clutter, passive radars, multistatic and cognitive radars. She serves on the editorial boards of IET Radar, Sonar and Navigation and Journal of Advances in Signal Processing, and she is the Editor-in-Chief of the IEEE Aerospace and Electronic Systems Magazine. She is a member of the IEEE Sensor Array Processing Technical Committees, of the IEEE AESS and of SP Boards of Governors, and Chair of the IEEE AESS Radar Panel.
Content
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
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