We propose a fast labeled multi-Bernoulli (LMB) filter that uses belief propagation for probabilistic data association. The complexity of our filter scales only linearly in the numbers of Bernoulli components and measurements, while the performance is comparable to or better than that of the Gibbs sampler-based LMB filter.


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    Titel :

    A Fast Labeled Multi-Bernoulli Filter Using Belief Propagation


    Beteiligte:


    Erscheinungsdatum :

    01.06.2020


    Format / Umfang :

    657147 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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