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29 October 1997 Classical and neural solutions for plot-to-track association
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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.
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
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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