Asymptotic estimate for missed/false-track probability in track-before-detect algorithms
Mark Copeland, Keith D. Kastella · Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 1995
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.