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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    Title :

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


    Contributors:


    Publication date :

    2016-10-01


    Size :

    1272874 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English