Analysis On Radar Image Classification Using Deep Learning

Abstract

The progress of the last 10 years of deep learning technology has inspired many fields of research, such as the processing of radar signal, speech and audio recognition, etc. Data representation acquired with Lidar or camera sensors are used for most prominent deep learning models, leaving automotive radars seldom used. Despite their vital potential in adverse weather conditions and their ability to seamlessly measure the range of an object and radial speed. Since radar signals have still not been used, the available benchmarking data is lacking. In the recent past, however, the application of radar data to various profound learning algorithms has been very interesting, since more datasets are being provided. This article aims to describe a new method of grading applied for the synthetic aperture radar (SAR), followed by fine tuning in such a grading scheme; Pre-trained architectures in the ImageNet database were used; the VGG 16 had actually been used as a feature extractor and the new classifier was trained based on the extracted features. The Dataset used is the data acquisition and recognition (MSTAR) of the Moving and Stationary Traget; for ten (10) different classes we have achieved a final accuracy of 97.91 percent.