On SVM for classification of real and synthetic radar signatures

This paper focuses on radar target identification using support vector machines (SVM). The radar features used in this study are impulse response features representing the down range profile of the target as seen by stepped frequency radar. The purpose of this paper is to shed additional light on the benefits of SVM in radar target identification (RTI) under various scenarios of adversity that are commonly addressed in the RTI literature. This paper attempts to maximize the performance of SVM in RTI but does not introduce new SVM kernels, or SVM training methods. The focus is on defining the rewards of SVM in target identification assuming a classifier that is presented with time domain signatures representing the target impulse response at a certain azimuth angle. In particular this paper focuses on assessing the SVM classifier performance in different scenarios, which are discussed in this paper