Range-Doppler Detection in Automotive Radar with Deep Learning

Abstract

This paper presents a comprehensive study on radar detection using deep learning with application to automotive vehicles. Automotive radars face complex target scenarios consisting of both small point targets and large extended targets. However, the current works on automotive radar detection mainly focus on point target detection. Moreover, those works use the complex range Doppler data for detection. In this paper, a deep learning based method for extended target detection is presented that takes advantage of augmented data for neural network training and prediction. Extensive simulations have been conducted to evaluate the proposed detection method and the results show performance improvement over a recent related method.