Radar target identification using probabilistic classification vector machines

Abstract

Radar target identification using probabilistic vector machines is investigated and tested using real radar data collected in a compact range for commercial aircraft models. Unlike relevance vector machines (RVM) that utilize zero-mean Gaussian prior for every weight for both negative and positive classes and are thus vulnerable to questionable (deceptive) vectors, probabilistic vector machines [2], alternatively, use nonnegative priors for the positive class and vice versa. This paper compares the performance of these machines with other target identification tools, and highlights scenarios where classification via a probabilistic vector machine is more plausible. The problem addressed in this paper is a M-ary target classification problem and is implemented as a set of pairwise comparisons between all competing hypotheses.