Evolutionary MCMC particle filtering for target cluster tracking

Avishy Carmi, Simon J. Godsill, Francois Septier

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

13 Scopus citations

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.

Original languageEnglish
Title of host publication2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, DSP/SPE 2009, Proceedings
Pages262-267
Number of pages6
DOIs
StatePublished - 8 Apr 2009
Externally publishedYes
Event2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, DSP/SPE 2009 - Marco Island, FL, United States
Duration: 4 Jan 20097 Jan 2009

Publication series

Name2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, DSP/SPE 2009, Proceedings

Conference

Conference2009 IEEE 13th Digital Signal Processing Workshop and 5th IEEE Signal Processing Education Workshop, DSP/SPE 2009
Country/TerritoryUnited States
CityMarco Island, FL
Period4/01/097/01/09

Keywords

  • Evolutionary MCMC
  • Markov chain Monte Carlo filtering
  • Multi cluster tracking
  • Variational Bayesian EM algorithm

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