Adaptive Radar Detection: Model-based, Data-driven and Hybrid Approaches

Coluccia explains how to adapt data-driven techniques for the problem of detecting radar, both individually and in combination with model-based approaches. His focus is on general space-time adaptive target detection against a background of interference consisting of clutter, possible jammers, and noise. He covers model-based adaptive radar detection; classification problem and data-driven tools; radar applications of machine learning; hybrid model-based and data-driven detection; and theories, interpretability, and other open issues. Annotation ©2022 Ringgold, Inc., Portland, OR (protoview.com)

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).