This paper proposes a fast implementation of the Labeled Multi-Bernoulli (LMB) filter based on a joint prediction and update scheme. The joint calculation prevents the treatment of insignificant hypotheses, e.g. considering the disappearance of an object with high existence probability which additionally generated a precise measurement in the received measurement set. Further, a Gibbs sampling approach for generating association hypotheses is presented which drastically reduces the computational complexity compared to Murty's ranked-assignment algorithm. The proposed Gibbs sampling implementation is compared to the standard implementation of the LMB filter using two scenarios: tracking vehicles using a multi-sensor setup on a German highway and extended object tracking in an urban scenario using Velodyne data.


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    Titel :

    A fast implementation of the Labeled Multi-Bernoulli filter using gibbs sampling


    Beteiligte:
    Reuter, Stephan (Autor:in) / Danzer, Andreas (Autor:in) / Stubler, Manuel (Autor:in) / Scheel, Alexander (Autor:in) / Granstrom, Karl (Autor:in)


    Erscheinungsdatum :

    01.06.2017


    Format / Umfang :

    949214 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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