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Proceedings Paper

ATR performance prediction using attributed scattering features
Author(s): Hung-Chih Chiang; Randolph L. Moses
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Paper Abstract

We present a method for estimating classification performance of a model-based synthetic aperture radar (SAR) automatic target recognition (ATR) system. Target classification is performed by comparing a feature vector extracted from a measured SAR image chip with a feature vector predicted from a hypothesized target class and pose. The feature vectors are matched using a Bayes likelihood metric that incorporates uncertainty in both the predicted and extracted feature vectors. We adopt an attributed scattering center model for the SAR features. The scattering attributes characterize frequency and angle dependence of each scattering center in correspondence the geometry of its physical scattering mechanism. We develop two Bayes matchers that incorporate two different solutions to the problem of correspondence between predicted and extracted scattering centers. We quantify classification performance with respect to the number of scattering center features. We also present classification results when the matchers assume incorrect feature uncertainty statistics.

Paper Details

Date Published: 13 August 1999
PDF: 12 pages
Proc. SPIE 3721, Algorithms for Synthetic Aperture Radar Imagery VI, (13 August 1999);
Show Author Affiliations
Hung-Chih Chiang, The Ohio State Univ. (United States)
Randolph L. Moses, The Ohio State Univ. (United States)

Published in SPIE Proceedings Vol. 3721:
Algorithms for Synthetic Aperture Radar Imagery VI
Edmund G. Zelnio, Editor(s)

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