Bayesian multiple-look updating applied to the SHARP ATR system

Matthew B. Ressler; Robert L. Williams; David C. Gross; Adrian P. Palomino

doi:10.1117/12.396354

24 August 2000 Bayesian multiple-look updating applied to the SHARP ATR system

Matthew B. Ressler, Robert L. Williams, David C. Gross, Adrian P. Palomino

Proceedings Volume 4053, Algorithms for Synthetic Aperture Radar Imagery VII; (2000) https://doi.org/10.1117/12.396354
Event: AeroSense 2000, 2000, Orlando, FL, United States

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.

Citation Download Citation

Matthew B. Ressler, Robert L. Williams, David C. Gross, and Adrian P. Palomino "Bayesian multiple-look updating applied to the SHARP ATR system", Proc. SPIE 4053, Algorithms for Synthetic Aperture Radar Imagery VII, (24 August 2000); https://doi.org/10.1117/12.396354

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