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Proceedings Paper

A probability of error-constrained sequential decision algorithm for data-rich automatic target recognition
Author(s): Irwin O. Reyes; Michael D. DeVore; Peter A. Beling; Barry M. Horowitz
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Paper Abstract

This paper illustrates an approach to sequential hypothesis testing designed not to minimize the amount of data collected but to reduce the overall amount of processing required, while still guaranteeing pre-specified conditional probabilities of error. The approach is potentially useful when sensor data are plentiful but time and processing capability are constrained. The approach gradually reduces the number of target hypotheses under consideration as more sensor data are processed, proportionally allocating time and processing resources to the most likely target classes. The approach is demonstrated on a multi-class ladar-based target recognition problem and compared with uniform-computation tests.

Paper Details

Date Published: 13 May 2010
PDF: 11 pages
Proc. SPIE 7696, Automatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI, 769615 (13 May 2010); doi: 10.1117/12.858293
Show Author Affiliations
Irwin O. Reyes, Univ. of Virginia (United States)
Michael D. DeVore, Barron Associates, Inc. (United States)
Peter A. Beling, Univ. of Virginia (United States)
Barry M. Horowitz, Univ. of Virginia (United States)


Published in SPIE Proceedings Vol. 7696:
Automatic Target Recognition XX; Acquisition, Tracking, Pointing, and Laser Systems Technologies XXIV; and Optical Pattern Recognition XXI
Firooz A. Sadjadi; David P. Casasent; Steven L. Chodos; Abhijit Mahalanobis; William E. Thompson; Tien-Hsin Chao, Editor(s)

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