Reasoning support and uncertainty prediction in model-based vision SAR ATR

Eric R. Keydel; Wayne D. Williams; Russell Sieron; Vasik G. Rajlich; Stephen A. Stanhope

doi:10.1117/12.357677

13 August 1999 Reasoning support and uncertainty prediction in model-based vision SAR ATR

Eric R. Keydel, Wayne D. Williams, Russell Sieron, Vasik G. Rajlich, Stephen A. Stanhope

Proceedings Volume 3721, Algorithms for Synthetic Aperture Radar Imagery VI; (1999) https://doi.org/10.1117/12.357677
Event: AeroSense '99, 1999, Orlando, FL, United States

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.

Citation Download Citation

Eric R. Keydel, Wayne D. Williams, Russell Sieron, Vasik G. Rajlich, and Stephen A. Stanhope "Reasoning support and uncertainty prediction in model-based vision SAR ATR", Proc. SPIE 3721, Algorithms for Synthetic Aperture Radar Imagery VI, (13 August 1999); https://doi.org/10.1117/12.357677

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