The standard formulation of the probability hypothesis density (PHD) and cardinalised PHD (CPHD) filters assumes that the target birth intensity is known a priori. In situations where the targets can appear anywhere in the surveillance volume this is clearly inefficient, since the target birth intensity needs to cover the entire state space. This paper presents a new extension of the PHD and CPHD filters, which distinguishes between the persistent and the newborn targets. This extension enables us to adaptively design the target birth intensity at each scan using the received measurements. Sequential Monte-Carlo (SMC) implementations of the resulting PHD and CPHD filters are presented and their performance studied numerically. The proposed measurement-driven birth intensity improves the estimation accuracy of both the number of targets and their spatial distribution.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Adaptive Target Birth Intensity for PHD and CPHD Filters


    Beteiligte:
    Ristic, B. (Autor:in) / Clark, D. (Autor:in) / Ba-Ngu Vo, (Autor:in) / Ba-Tuong Vo, (Autor:in)


    Erscheinungsdatum :

    2012-04-01


    Format / Umfang :

    1061092 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    A Partially Uniform Target Birth Model for Gaussian Mixture PHD/CPHD Filtering

    Beard, Michael / Vo, Ba Tuong / Vo, Ba-Ngu et al. | IEEE | 2013


    Multisensor CPHD filter

    Nannuru, Santosh / Blouin, Stephane / Coates, Mark et al. | IEEE | 2016



    Hybrid multi-Bernoulli and CPHD filters for superpositional sensors

    Nannuru, Santosh / Coates, Mark | IEEE | 2015