The Probability Hypothesis Density (PHD) filter is a recent solution to the multi-target filtering problem. Because the PHD filter is not computable, several implementations have been proposed including the Gaussian Mixture (GM) approximations and Sequential Monte Carlo (SMC) methods. In this paper, we propose a marginalized particle PHD filter which improves the classical solutions when used in stochastic systems with partially linear substructure.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Marginalized particle PHD filters for multiple object Bayesian filtering


    Beteiligte:


    Erscheinungsdatum :

    01.04.2014


    Format / Umfang :

    696626 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Marginalized particle filters for mixed linear/nonlinear state-space models

    Schon, T. / Gustafsson, F. / Nordlund, P.J. | Tema Archiv | 2005




    Three-Degree-of-Freedom Estimation of Agile Space Objects Using Marginalized Particle Filters

    Coder, Ryan D. / Holzinger, Marcus J. / Linares, Richard | AIAA | 2018


    The marginalized particle filter in practice

    Schon, T.B. / Karlsson, R. / Gustafsson, F. | IEEE | 2006