Evolutionary MCMC Particle Filtering for Target Cluster Tracking

Abstract : A new filtering algorithm is presented for tracking multiple clusters of coordinated targets. Based on a Markov chain Monte Carlo sampling mechanization, the new algorithm maintains a discrete approximation of the filtering density of the clusters' state. The filter's tracking efficiency is enhanced by incorporating two stages into the basic Metropolis-Hastings sampling scheme: 1) Interaction. Improved moves are generated by exchanging genetic material between samples from different realizations of the same chain, and 2) Optimization. Optimized proposals in terms of likelihood are obtained using a Bayesian extension of the EM algorithm. In addition, a method is devised based on the Akaike information criterion (AIC) for eliminating fictitious clusters that may appear when tracking in a highly cluttered environment. The algorithm's performance is assessed and demonstrated in a tracking scenario consisting of several hundreds targets which form up to six distinct clusters in a highly cluttered environment.
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Contributor : François Septier <>
Submitted on : Wednesday, February 16, 2011 - 4:44:28 PM
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Avishy Carmi, Simon Godsill, François Septier. Evolutionary MCMC Particle Filtering for Target Cluster Tracking. IEEE 13th DSP Workshop and the 5th SPE Workshop, Jan 2009, Marco Island, Florida, United States. pp.262-267. ⟨hal-00566593⟩

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