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Proceedings Paper

Comparison of mean-field tracker and joint probabilistic data association tracker in high-clutter environments
Author(s): Keith D. Kastella; Charles Lutes
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Paper Abstract

This paper briefly reviews the development of the mean-field event-averaged maximum likelihood estimation (MFEAMLE) tracker and compares its tracking performance with that of a joint probabilistic data association (JPDA) filter. The JPDA and MFEAMLE approaches are similar in that they both average over measurement to track associations. However, there are several features of MFEAMLE that improve its estimation performance at high target and clutter densities while simplifying the required computation enough to make real-time performance feasible. To enhance tracking of close targets, the filter explicitly models the error correlations that occur between such target pairs, rather than assuming that they are independent. These error correlations arise from the measurement to track association ambiguity present when target separations are comparable to the measurement errors in the sensors. In order to reduce the computational load, a mean-field approximation is used to perform the summation over all associations. In performance comparison on simulated data, smaller average errors and less track loss were obtained for the MFEAMLE tracker than with JPDA.

Paper Details

Date Published: 1 September 1995
PDF: 7 pages
Proc. SPIE 2561, Signal and Data Processing of Small Targets 1995, (1 September 1995); doi: 10.1117/12.217698
Show Author Affiliations
Keith D. Kastella, Loral Corp. (United States)
Charles Lutes, 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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