Random finite sets (RFSs) are natural representations of multitarget states and observations that allow multisensor multitarget filtering to fit in the unifying random set framework for data fusion. Although the foundation has been established in the form of finite set statistics (FISST), its relationship to conventional probability is not clear. Furthermore, optimal Bayesian multitarget filtering is not yet practical due to the inherent computational hurdle. Even the probability hypothesis density (PHD) filter, which propagates only the first moment (or PHD) instead of the full multitarget posterior, still involves multiple integrals with no closed forms in general. This article establishes the relationship between FISST and conventional probability that leads to the development of a sequential Monte Carlo (SMC) multitarget filter. In addition, an SMC implementation of the PHD filter is proposed and demonstrated on a number of simulated scenarios. Both of the proposed filters are suitable for problems involving nonlinear nonGaussian dynamics. Convergence results for these filters are also established.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Sequential Monte Carlo methods for multitarget filtering with random finite sets


    Contributors:
    Vo, B.-N. (author) / Singh, S. (author) / Doucet, A. (author)


    Publication date :

    2005-10-01


    Size :

    1065572 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Fusion-Based Multidetection Multitarget Tracking With Random Finite Sets

    Gao, Lin / Battistelli, Giorgio / Chisci, Luigi et al. | IEEE | 2021


    Sequential Monte Carlo Filtering with Gaussian Mixture Sampling

    Yun, Sehyun / Zanetti, Renato | AIAA | 2019



    Improved sequential Monte Carlo filtering for ballistic target tracking

    Bruno, M.G.S. / Pavlov, A. | Tema Archive | 2005