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Proceedings Paper

Asymptotic estimate for missed/false-track probability in track-before-detect algorithms
Author(s): Mark Copeland; Keith D. Kastella
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Paper Abstract

This article characterizes asymptotic limits for the error probabilities that arise while testing for the detection of targets in the presence of clutter. The hypothesis test decision regions are determined by the discrimination function. The function is the basic measure of the information contained in the measurements. While the Neyman-Pearson Theorem specifies the optimum decision regions, it does not specify the detection performance in terms of the error probabilities. Asymptotic bounds expressed as analytical functions allows us to determine the effect of the decision threshold, the clutter density, and the number of measurements on the error probabilities; thus indicating the effectiveness of the testing procedure.

Paper Details

Date Published: 1 September 1995
PDF: 8 pages
Proc. SPIE 2561, Signal and Data Processing of Small Targets 1995, (1 September 1995); doi: 10.1117/12.217725
Show Author Affiliations
Mark Copeland, Univ. of Minnesota (United States)
Keith D. Kastella, Loral Corp. (United States)

Published in SPIE Proceedings Vol. 2561:
Signal and Data Processing of Small Targets 1995
Oliver E. Drummond, Editor(s)

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