Langevin Monte Carlo filtering for target tracking

Fernando Iglesias Garcia, Melanie Bocquel, Hans Driessen

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

    4 Citations (Scopus)

    Abstract

    This paper introduces the Langevin Monte Carlo Filter (LMCF), a particle filter with a Markov chain Monte Carlo algorithm which draws proposals by simulating Hamiltonian dynamics. This approach is well suited to non-linear filtering problems in high dimensional state spaces where the bootstrap filter requires an impracticably large number of particles. The simulation of Hamiltonian dynamics is motivated by leveraging more model knowledge in the proposal design. In particular, the gradient of the posterior energy function is used to draw new samples with high probability of acceptance. Furthermore, the introduction of auxiliary variables (the so-called momenta) ensures that new samples do not collapse at a single mode of the posterior density. In comparison with random-walk Metropolis, the LMC algorithm has been proven more efficient as the state dimension increases. Therefore, we are able to verify through experiments that our LMCF is able to attain multi-target tracking using small number of particles when other MCMC-based particle filters relying on random-walk Metropolis require a considerably larger particle number. As a conclusion, we claim that performing little additional work for each particle (in our case, computing likelihood energy gradients) turns out to be very effective as it allows to greatly reduce the number of particles while improving tracking performance.
    Original languageUndefined
    Title of host publicationProceedings of the 18th International Conference on Information Fusion
    PublisherIEEE
    Pages82-89
    Number of pages8
    ISBN (Print)978-0-9824-4386-6
    Publication statusPublished - 2015

    Publication series

    Name
    PublisherIEEE

    Keywords

    • EWI-26839
    • MCMC
    • Tracking
    • METIS-316048
    • HMC
    • IR-99465
    • Particle filter

    Cite this

    Iglesias Garcia, F., Bocquel, M., & Driessen, H. (2015). Langevin Monte Carlo filtering for target tracking. In Proceedings of the 18th International Conference on Information Fusion (pp. 82-89). IEEE.