This paper presents a multitarget tracking particle filter for general track-before-detect measurement models. The particle filter is presented in the random-finite-set framework and uses a labeled multi-Bernoulli approximation. I also present a label-switching improvement algorithm based on Markov-chain Monte Carlo methods that is expected to increase filter performance if targets are in close proximity for a sufficiently long time. The particle filter is tested in two challenging numerical examples.


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    Titel :

    Track-before-detect labeled multi-bernoulli particle filter with label switching


    Beteiligte:


    Erscheinungsdatum :

    2016-10-01


    Format / Umfang :

    1272874 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch