Deep learning for radar

Abstract

Motivated by the recent advances in deep learning, we lay out a vision of how deep learning techniques can be used in radar. Specifically, our discussion focuses on the use of deep learning to advance the state-of-the-art in radar imaging. While deep learning can be directly applied to automatic target recognition (ATR), the relevance of these techniques in other radar problems is not obvious. We argue that deep learning can play a central role in advancing the state-of-the-art in a wide range of radar imaging problems, discuss the challenges associated with applying these methods, and the potential advancements that are expected. We lay out an approach to design a network architecture based on the specific structure of the synthetic aperture radar (SAR) imaging problem that augments learning with traditional SAR modelling. This framework allows for capture of the non-linearity of the SAR forward model. Furthermore, we demonstrate how this process can be used to learn and compensate for trajectory based phase error for the autofocus problem.