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Proceedings Paper

Bayesian multiple-look updating applied to the SHARP ATR system
Author(s): Matthew B. Ressler; Robert L. Williams; David C. Gross; Adrian P. Palomino
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Paper Abstract

This study summarizes recent algorithmic enhancements made to the AFRL/SNAA Systems-Oriented High Range Resolution (HRR) Automatic Recognition Program (SHARP) in the areas of multiple-look updating and sensor fusion. The benefits in improved 1-D Automatic Target Recognition (ATR) performance resulting from these enhancements are quantified. The study incorporates a unique method of estimating Bayesian probabilities by exploiting the fact that 1-D range profiles formed from Moving and Stationary Target Acquisition and Recognition (MSTAR) target chips overlap in azimuth. Thus, multiple samples of range profiles exist for the same target at very similar viewing aspects, but from independent passes of the sensor. ATR performance using the Bayesian technique is characterized first for an updating architecture that fuses probabilities over a fixed number of looks and then makes a 'classify or reject' decision. A second proposed architecture that makes a 'classify, reject, or take another measurement' decision is also analyzed. For both postulated architectures, ATR performance enhancement over the SHARP baseline updating procedure is quantified.

Paper Details

Date Published: 24 August 2000
PDF: 10 pages
Proc. SPIE 4053, Algorithms for Synthetic Aperture Radar Imagery VII, (24 August 2000); doi: 10.1117/12.396354
Show Author Affiliations
Matthew B. Ressler, Toyon Research Corp. (United States)
Robert L. Williams, Air Force Research Lab. (United States)
David C. Gross, Veridian Engineering (United States)
Adrian P. Palomino, Veridian Engineering (United States)

Published in SPIE Proceedings Vol. 4053:
Algorithms for Synthetic Aperture Radar Imagery VII
Edmund G. Zelnio, Editor(s)

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