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Proceedings Paper

Radar target identification using probabilistic classification vector machines
Author(s): I. Jouny
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Paper 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.

Paper Details

Date Published: 12 May 2016
PDF: 9 pages
Proc. SPIE 9844, Automatic Target Recognition XXVI, 98440L (12 May 2016); doi: 10.1117/12.2224103
Show Author Affiliations
I. Jouny, Lafayette College (United States)


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

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