Multiple object tracking using evolutionary MCMC-based particle algorithms

F. Septier, A. Carmi, S. K. Pang, S. J. Godsill

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

6 Scopus citations


Algorithms are presented for detection and tracking of multiple clusters of co-ordinated targets. Based on a Markov chain Monte Carlo sampling mechanization, the new algorithms maintain a discrete approximation of the filtering density of the clusters' state. The filters' tracking efficiency is enhanced by incorporating various sampling improvement strategies into the basic Metropolis-Hastings scheme. Thus, an evolutionary stage consisting of two primary steps is introduced: 1) producing a population of different chain realizations, and 2) exchanging genetic material between samples in this population. The performance of the resulting evolutionary filtering algorithms is demonstrated in two different settings. In the first, both group and target properties are estimated whereas in the second, which consists of a very large number of targets, only the clustering structure is maintained.

Original languageEnglish
Title of host publication15th Symposium on System Identification, SYSID 2009 - Preprints
Number of pages6
EditionPART 1
StatePublished - 1 Dec 2009
Externally publishedYes
Event15th IFAC Symposium on System Identification, SYSID 2009 - Saint-Malo, France
Duration: 6 Jul 20098 Jul 2009

Publication series

NameIFAC Proceedings Volumes (IFAC-PapersOnline)
NumberPART 1
ISSN (Print)1474-6670


Conference15th IFAC Symposium on System Identification, SYSID 2009


  • Estimation algorithms
  • Genetic algorithms
  • Monte Carlo method
  • Multitarget tracking
  • Recursive estimation

ASJC Scopus subject areas

  • Control and Systems Engineering


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