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Proceedings Paper

Reasoning support and uncertainty prediction in model-based vision SAR ATR
Author(s): Eric R. Keydel; Wayne D. Williams; Russell Sieron; Vasik G. Rajlich; Stephen A. Stanhope
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Paper Abstract

The MSTAR automatic target recognition (ATR) system recognizes targets by matching features predicted from a CAD model against features extracted from the unknown signature. In addition to generating signature features with high fidelity, the online Predictor in the MSTAR system must provide information that assists in efficient search of the hypothesis space as well as accounting for uncertainties in the prediction process. In this paper, we describe two capabilities implemented in the MSTAR Predictor to support this process. The first exploits the inherent traceback between predicted features and the CAD model that is integral to the predictor to enable component-wise scoring of candidate hypotheses. The second is the generation of probability density functions that characterize the fluctuation of amplitudes in the predicted signatures. The general approach for both of these is described, and example results are presented.

Paper Details

Date Published: 13 August 1999
PDF: 12 pages
Proc. SPIE 3721, Algorithms for Synthetic Aperture Radar Imagery VI, (13 August 1999); doi: 10.1117/12.357677
Show Author Affiliations
Eric R. Keydel, ERIM International, Inc. (United States)
Wayne D. Williams, ERIM International, Inc. (United States)
Russell Sieron, ERIM International, Inc. (United States)
Vasik G. Rajlich, ERIM International, Inc. (United States)
Stephen A. Stanhope, ERIM International, Inc. (United States)

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

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