Asymptotic estimate for missed/false-track probability in track-before-detect algorithms

Mark Copeland; Keith D. Kastella

doi:10.1117/12.217725

1 September 1995 Asymptotic estimate for missed/false-track probability in track-before-detect algorithms

Mark Copeland, Keith D. Kastella

Proceedings Volume 2561, Signal and Data Processing of Small Targets 1995; (1995) https://doi.org/10.1117/12.217725
Event: SPIE's 1995 International Symposium on Optical Science, Engineering, and Instrumentation, 1995, San Diego, CA, United States

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.

Citation Download Citation

Mark Copeland and Keith D. Kastella "Asymptotic estimate for missed/false-track probability in track-before-detect algorithms", Proc. SPIE 2561, Signal and Data Processing of Small Targets 1995, (1 September 1995); https://doi.org/10.1117/12.217725

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