Machine Learning for Radar Sensing & Imaging

Machine Learning (ML) has dramatically advanced the state-of-the-art for many problems in science and engineering. Inspired by these developments, ML has drawn increasing attention in radar signal processing. Predominantly, applications of ML have focused on the advancement of automatic target recognition algorithms across a multitude of radar data types, including synthetic aperture radar, micro-Doppler signatures, and high-resolution range profiles. Performance gains achieved in the area of classification have spurred research into potential application of ML to other facets of radar design – The focus of this talk will be these novel perspectives on the role of machine learning in the radar sensing process. I will present innovative techniques that leverage machine and deep learning for the purposes of passive radar, image reconstruction, radar resource management, waveform estimation and design, and automatic target recognition. Together this talk will draw attention to new and innovative ideas for application of ML to radar sensing and imaging.