A labeled multi-Bernoulli (LMB) filter is presented to perform multitarget tracking (MTT) for the glint noise. The measurement noise is modeled as a multivariate Student-$t$ process. The variational Bayesian method is applied in the LMB framework with the augmented state. The predictive likelihood is calculated via minimizing the Kullback–Leibler divergence by the variational lower bound. Simulation results show that our approach is effective in MTT with the glint noise.


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    Title :

    The Labeled Multi-Bernoulli Filter for Multitarget Tracking With Glint Noise


    Contributors:
    Dong, Peng (author) / Jing, Zhongliang (author) / Leung, Henry (author) / Shen, Kai (author) / Li, Minzhe (author)


    Publication date :

    2019-10-01


    Size :

    1771830 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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