Radar target identification using probabilistic classification vector machines

I. Jouny

doi:10.1117/12.2224103

12 May 2016 Radar target identification using probabilistic classification vector machines

I. Jouny

Proceedings Volume 9844, Automatic Target Recognition XXVI; 98440L (2016) https://doi.org/10.1117/12.2224103
Event: SPIE Defense + Security, 2016, Baltimore, MD, United States

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.

Citation Download Citation

I. Jouny "Radar target identification using probabilistic classification vector machines", Proc. SPIE 9844, Automatic Target Recognition XXVI, 98440L (12 May 2016); https://doi.org/10.1117/12.2224103

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