Modern radar detection theory

"Modern Radar Detection Theory is a comprehensive reference on the latest developments in adaptive radar detection. It explores the key algorithms used with advanced radar systems, such as airborne early warning, capable of operating in challenging scenarios with a plurality of man-made and natural interference sources. Results on real radar data are extensively discussed. Detector design techniques based on a variety of criteria are thoroughly presented and Constant False Alarm Rate (CFAR) issues are discussed. Performance analyses representative of practical airborne as well as ground-based and shipborne radars situations are shown." --Publisher's website.

Introduction to radar detection / Antonio De Maio, Maria S. Greco, and Danilo Orlando -- Radar detection in white gaussian noise: A GLRT framework / Ernesto Conte, Antonio De Maio, and Guolong Cui -- Subspace detection for adaptive radar: Detectors and performance analysis / Ram S. Raghavan, Shawn Kraut, and Christ D. Richmond -- Two-stage detectors for point-like targets in Gaussian interference with unknown spectral properties / Antonio De Maio, Chengpeng Hao, and Danilo Orlando -- Bayesian radar detection in interference / Pu Wang, Hongbin Li, and Braham Himed -- Adaptive radar detection for sample-starved Gaussian training conditions / Yuri I. Abramovich and Ben A. Johnson -- Compound-Gaussian models and target detection: A unified view / K. James Sangston, Maria S. Greco, and Fulvio Gini -- Covariance matrix estimation in SIRV and elliptical processes and their applications in radar detection / Jean-Philippe Ovarlez, Frédéric Pascal, and Philippe Forster -- 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.