Acceptance probability of IP-MCMC-PF: revisited

Fernando Iglesias Garcia, Melanie Bocquel, Pranab K. Mandal, Hans Driessen

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

    1 Citation (Scopus)

    Abstract

    Tracking of multiple objects via particle filtering faces the difficulty of dealing effectively with high dimensional state spaces. One efficient solution consists of integrating Markov chain Monte Carlo (MCMC) sampling at the core of the particle filter. To accomplish such integration, a few different approaches have been proposed in the literature during the last decade. In this paper, we introduce the derivation of the acceptance probability of the interacting population MCMC particle filter (IP-MCMC-PF), one of the most recent approaches to MCMC-based particle filtering. Additionally, we show that the previous expression known in the literature was incomplete.
    Original languageUndefined
    Title of host publication10th Workshop: Sensor Data Fusion: Trends, Solutions, Applications
    Place of PublicationBonn
    PublisherIEEE
    Pages1-6
    Number of pages6
    ISBN (Print)978-1-4673-7175-9
    DOIs
    Publication statusPublished - 6 Oct 2015
    EventSensor Data Fusion: Trends, Solutions, Applications, SDF 2015 - Bonn, Germany
    Duration: 6 Oct 20158 Oct 2015

    Publication series

    Name
    PublisherIEEE

    Workshop

    WorkshopSensor Data Fusion: Trends, Solutions, Applications, SDF 2015
    Abbreviated titleSDF
    CountryGermany
    CityBonn
    Period6/10/158/10/15

    Keywords

    • EWI-26724
    • MCMC
    • IR-99227
    • Particle filter
    • METIS-315543
    • Tracking

    Cite this

    Iglesias Garcia, F., Bocquel, M., Mandal, P. K., & Driessen, H. (2015). Acceptance probability of IP-MCMC-PF: revisited. In 10th Workshop: Sensor Data Fusion: Trends, Solutions, Applications (pp. 1-6). Bonn: IEEE. https://doi.org/10.1109/SDF.2015.7347699