Classical and neural solutions for plot-to-track association

Michel Winter; Valerie Schmidlin; Gerard Favier

doi:10.1117/12.279537

29 October 1997 Classical and neural solutions for plot-to-track association

Michel Winter, Valerie Schmidlin, Gerard Favier

Proceedings Volume 3163, Signal and Data Processing of Small Targets 1997; (1997) https://doi.org/10.1117/12.279537
Event: Optical Science, Engineering and Instrumentation '97, 1997, San Diego, CA, United States

Abstract

This paper presents and compares two alternative classes of solutions to the plot-to-track association problem. The first class of solutions relies on classical approaches of signal processing, principally based on the Bayes theory, that is to say the nearest- neighbor filter, the probabilistic data association filter and the joint probabilistic data association filter. The data association problem can be reduced to a combinatorial optimization problem, for which the time needed to obtain the exact solution grows drastically with the problem size. This is the reason why, in most cases, we do not look for the best solution, but rather for a good solution, reachable in a reasonable computation time. Consequently, neural networks are an interesting alternative to classical solutions. We first review several neural models: Hopfield networks, Boltzmann machine, mean filed approximation networks and our approach derived from the Hopfield model. Then we present some simulation results that enable to compare the various techniques for a general assignment problem and for the multitarget tracking problem.

Citation Download Citation

Michel Winter, Valerie Schmidlin, and Gerard Favier "Classical and neural solutions for plot-to-track association", Proc. SPIE 3163, Signal and Data Processing of Small Targets 1997, (29 October 1997); https://doi.org/10.1117/12.279537

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