Object Surface Estimation from Radar Images

Abstract

In this paper we develop a deep neural network (DNN) method for estimating the object surface from radar 2D image (azimuth-range). The DNN is designed to maintain the input image angular resolution and produces two outputs per each angle, which are a classification bit and a regression value. The classification bit determines whether there is a reflection point per each angle, and the regression value is the estimated reflection range. We have developed a statistical simulation model that approximates the statistics of the radar image, and trained the network with synthetically generated examples from the simulation model. The network showed good performance on radar images that were obtained from real radar measurements, and also showed to outperform the common CFAR reference method.