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Proceedings Paper

Performance models for hypothesis-level fusion of multilook SAR ATR
Author(s): William C. Snyder; Gil J. Ettinger
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Paper Abstract

We present the theoretical basis and a top level system design for estimating and predicting the uncertainty from single and multiple-look model-based automatic target recognition (ATR). Uncertainty estimation is used in decision making based on the probability of correct identification and the probability of a false alarm for a given ATR result. Uncertainty prediction provides a basis for asset management by establishing the value of additional looks at a target. A number of first principles theoretical models have been developed based on information theory and physics. These generally bound performance under idealized conditions. Our hypothesis test approach is designed to support operational uncertainty estimation and prediction based on statistics from parameterized models, simulations, and measurements. A significant challenge that we investigate is generating the probability density of the test statistic under the null hypothesis, which contains un-modeled types and natural clutter. Another challenge we address is establishing uncertainty under multiple look fusion.

Paper Details

Date Published: 12 September 2003
PDF: 12 pages
Proc. SPIE 5095, Algorithms for Synthetic Aperture Radar Imagery X, (12 September 2003); doi: 10.1117/12.487036
Show Author Affiliations
William C. Snyder, ALPHATECH, Inc. (United States)
Gil J. Ettinger, ALPHATECH, Inc. (United States)


Published in SPIE Proceedings Vol. 5095:
Algorithms for Synthetic Aperture Radar Imagery X
Edmund G. Zelnio; Frederick D. Garber, Editor(s)

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