The Poisson multi-Bernoulli mixture (PMBM) is a multiobject conjugate prior for the closed-form Bayes random finite set filter. The extended object PMBM filter provides a closed-form solution for multiple extended object filtering with standard models. This article considers computationally lighter alternatives to the extended object PMBM filter by propagating a Poisson multi-Bernoulli (PMB) density through the filtering recursion. A new local hypothesis representation is presented, where each measurement creates a new Bernoulli component. This facilitates the developments of methods for efficiently approximating the PMBM posterior density after the update step as a PMB. Based on the new hypothesis representation, two approximation methods are presented: one is based on the track-oriented multi-Bernoulli (MB) approximation, and the other is based on the variational MB approximation via Kullback–Leibler divergence minimization. The performance of the proposed PMB filters with gamma Gaussian inverse-Wishart implementations are evaluated in a simulation study.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Poisson Multi-Bernoulli Approximations for Multiple Extended Object Filtering


    Beteiligte:


    Erscheinungsdatum :

    01.04.2022


    Format / Umfang :

    2055541 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Poisson Multi-Bernoulli Mixture Conjugate Prior for Multiple Extended Target Filtering

    Granstrom, Karl / Fatemi, Maryam / Svensson, Lennart | IEEE | 2020



    Data-Driven Clustering and Bernoulli Merging for the Poisson Multi-Bernoulli Mixture Filter

    Fontana, Marco / Garcia-Fernandez, Angel F. / Maskell, Simon | IEEE | 2023


    Poisson Multi-Bernoulli Mixtures for Sets of Trajectories

    Granstrom, Karl / Svensson, Lennart / Xia, Yuxuan et al. | IEEE | 2025