This article proposes a Gaussian filtering method to approximate the single-target updates and normalizing constants for multitarget tracking with nonlinear, non-Gaussian measurements, and a state-dependent probability of detection. The Gaussian approximation is based on the posterior linearization technique, which seeks the optimal affine approximation of the nonlinearities in a mean square error sense. The normalizing constant is approximated using sigma-points based on the posterior. The proposed approach is implemented in a Poisson multi-Bernoulli mixture filter and compared against standard methods to approximate single-target posteriors and normalizing constants in two range-bearings tracking scenarios.


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    Title :

    A Gaussian Filtering Method for Multitarget Tracking With Nonlinear/Non-Gaussian Measurements


    Contributors:


    Publication date :

    2021-10-01


    Size :

    576850 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English