Multitarget tracking with interacting population-based MCMC-PF

Melanie Bocquel, Hans Driessen, Arunabha Bagchi

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

    12 Citations (Scopus)

    Abstract

    In this paper we address the problem of tracking multiple targets based on raw measurements by means of Particle filtering. This strategy leads to a high computational complexity as the number of targets increases, so that an efficient implementation of the tracker is necessary. We propose a new multitarget Particle Filter (PF) that solves such challenging problem. We call our filter Interacting Population-based MCMC-PF (IP-MCMC-PF) since our approach is based on parallel usage of multiple population-based Metropolis-Hastings (M-H) samplers. Furthermore, to improve the chains mixing properties, we exploit genetic alike moves performing interaction between the Markov Chain Monte Carlo (MCMC) chains. Simulation analyses verify a dramatic reduction in terms of computational time for a given track accuracy, and an increased robustness w.r.t. conventional MCMC based PF.
    Original languageUndefined
    Title of host publicationProceedings of the 15th International Conference on Information Fusion (FUSION 2012)
    Place of PublicationSingapore
    PublisherIEEE
    Pages74-81
    Number of pages8
    ISBN (Print)978-1-4673-0417-7
    Publication statusPublished - Jul 2012
    Event15th International Conference on Information Fusion, FUSION 2012 - Singapore, Singapore, Singapore
    Duration: 9 Jul 201212 Jul 2012
    Conference number: 15

    Publication series

    Name
    PublisherIEEE

    Conference

    Conference15th International Conference on Information Fusion, FUSION 2012
    Abbreviated titleFUSION 2012
    CountrySingapore
    CitySingapore
    Period9/07/1212/07/12
    Other9-12 July 2012

    Keywords

    • IR-83551
    • EWI-22762
    • METIS-296185

    Cite this

    Bocquel, M., Driessen, H., & Bagchi, A. (2012). Multitarget tracking with interacting population-based MCMC-PF. In Proceedings of the 15th International Conference on Information Fusion (FUSION 2012) (pp. 74-81). Singapore: IEEE.