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.

Date Published: 12 May 2016
PDF: 9 pages
Proc. SPIE 9844, Automatic Target Recognition XXVI, 98440L (12 May 2016); doi: 10.1117/12.2224103

Published in SPIE Proceedings Vol. 9844:
Automatic Target Recognition XXVI
Firooz A. Sadjadi; Abhijit Mahalanobis, Editor(s)