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Proceedings Paper

Performance modeling for automatic target recognition systems
Author(s): Anne Catlin; Lemuel R. Myers; Kenneth W. Bauer; Steven K. Rogers; Randy P. Broussard
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Paper Abstract

This paper explores using linear regression and artificial neural networks (ANN) to model the performance of an ATR algorithm based on a given set of data. Here, a probability of detection response surfaces as a function of relevant parameters is simulated. It is then shown that this surface can be approximated using either linear regression or an ANN with good results. These regression surfaces can provide valuable information to the ATR developer/customer in terms of trying to predict ATR performance in untested areas. The application of this ATR performance modeling methodology becomes clear when we consider applying it to a common problem, such as air-to-ground target detection, where the changing parameters of the target can give a good set of data points from which to build the response curve.

Paper Details

Date Published: 28 July 1997
PDF: 9 pages
Proc. SPIE 3070, Algorithms for Synthetic Aperture Radar Imagery IV, (28 July 1997); doi: 10.1117/12.281555
Show Author Affiliations
Anne Catlin, Air Force Institute of Technology (United States)
Lemuel R. Myers, Air Force Institute of Technology (United States)
Kenneth W. Bauer, Air Force Institute of Technology (United States)
Steven K. Rogers, Air Force Institute of Technology (United States)
Randy P. Broussard, Air Force Institute of Technology (United States)


Published in SPIE Proceedings Vol. 3070:
Algorithms for Synthetic Aperture Radar Imagery IV
Edmund G. Zelnio, Editor(s)

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