This paper presents a Poisson multi-Bernoulli mixture (PMBM) conjugate prior for multiple extended object filtering. A Poisson point process is used to describe the existence of yet undetected targets, while a multi-Bernoulli mixture describes the distribution of the targets that have been detected. The prediction and update equations are presented for the standard transition density and measurement likelihood. Both the prediction and the update preserve the PMBM form of the density, and in this sense, the PMBM density is a conjugate prior. However, the unknown data associations lead to an intractably large number of terms in the PMBM density, and approximations are necessary for tractability. A gamma Gaussian inverse Wishart implementation is presented, along with methods to handle the data association problem. A simulation study shows that the extended target PMBM filter performs well in comparison to the extended target $\delta$-generalized labelled multi-Bernoulli and LMB filters. An experiment with Lidar data illustrates the benefit of tracking both detected and undetected targets.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Poisson Multi-Bernoulli Mixture Conjugate Prior for Multiple Extended Target Filtering


    Contributors:


    Publication date :

    2020-02-01


    Size :

    2585853 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




    Poisson Multi-Bernoulli Approximations for Multiple Extended Object Filtering

    Xia, Yuxuan / Granstrom, Karl / Svensson, Lennart et al. | IEEE | 2022


    A Gaussian Mixture Extended-Target Multi-Bernoulli Filter

    Zhang, G. / Lian, F. / Han, C. et al. | British Library Online Contents | 2014


    Data-Driven Clustering and Bernoulli Merging for the Poisson Multi-Bernoulli Mixture Filter

    Fontana, Marco / Garcia-Fernandez, Angel F. / Maskell, Simon | IEEE | 2023