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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    Title :

    A Fast Labeled Multi-Bernoulli Filter Using Belief Propagation


    Contributors:


    Publication date :

    2020-06-01


    Size :

    657147 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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