This book shows you how to adopt data-driven techniques for the problem of radar detection, both per se and in combination with model-based approaches. In particular, the focus is on space-time adaptive target detection against a background of interference consisting of clutter, possible jammers, and noise. It is a handy, concise reference for many classic (model-based) adaptive radar detection schemes as well as the most popular machine learning techniques (including deep neural networks) and helps you identify suitable data-driven approaches for radar detection and the main related issues. You’ll learn how data-driven tools relate to, and can be coupled or hybridized with, traditional adaptive detection statistics; understand fundamental concepts, schemes, and algorithms from statistical learning, classification, and neural networks domains. The book also walks you through how these concepts and schemes have been adapted for the problem of radar detection in the literature and provides you with a methodological guide for the design, illustrating different possible strategies. You’ll be equipped to develop a unified view, under which you can exploit the new possibilities of the data-driven approach even using simulated data. This book is an excellent resource for Radar professionals and industrial researchers, postgraduate students in electrical engineering and the academic community.
Model-based adaptive radar detection, Classification Problems and Data-Driven Tools, Radar applications of machine learning, Hybrid model-based and data-driven detection, Theories, interpretability, and other open issues
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Angelo Coluccia
received the MSc degree summa cum laude in Telecommunication Engineering in 2007 and the PhD degree in Information Engineering in 2011. He is currently an Associate Professor of Telecommunications at the Department of Engineering, University of Salento, Lecce, Italy. He is Senior Member of IEEE, Member of the Sensor Array and Multichannel Technical Committee for the IEEE Signal Processing Society, and Member of the Technical Area Committee in Signal Processing for Multisensor Systems of EURASIP (European Association for Signal Processing).