The single-sensor probability hypothesis density (PHD) and cardinalized probability hypothesis density (CPHD) filters have been developed in the literature using the random finite set framework. The existing multisensor extensions of these filters have limitations such as sensor-order dependence, numerical instability, or high computational requirements. In this paper, we derive update equations for the multisensor CPHD filter. The multisensor PHD filter is derived as a special case. Exact implementation of the multisensor CPHD involves sums over all partitions of the measurements from different sensors and is thus intractable. We propose a computationally tractable approximation that combines a greedy measurement partitioning algorithm with the Gaussian mixture representation of the PHD. Our greedy approximation method allows the user to control the trade-off between computational overhead and approximation accuracy.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Multisensor CPHD filter


    Contributors:


    Publication date :

    2016-08-01


    Size :

    1024951 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Track initialization for TOMHT using auxiliary CPHD filter

    Chen, Xin / Tharmarasa, R. / Kirubarajan, T. et al. | IEEE | 2012


    CPHD FILTER ADDRESSING OCCLUSIONS WITH PEDESTRIANS AND VEHICLES TRACKING

    Lamard, L. / Chapuis, R. / Boyer, J. et al. | British Library Conference Proceedings | 2013


    CPHD filter addressing occlusions with pedestrians and vehicles tracking

    Lamard, Laetitia / Chapuis, Roland / Boyer, Jean-Philippe | IEEE | 2013



    A Tractable Forward– Backward CPHD Smoother

    Nagappa, Sharad / Delande, Emmanuel D. / Clark, Daniel E. et al. | IEEE | 2017

    Free access