In tracking applications, the target state (e.g, position, velocity) can be estimated by processing the measurements collected from all deployed sensors at a central node. The estimation performance significantly relies on the accuracy of the sensor positions/rotations when data fusion is conducted. Since in practice precise knowledge of this sensor information may not be available, in this paper two Sequential Monte Carlo (SMC) approaches are proposed to jointly estimate the target state and resolve the sensor position uncertainty. The first one uses the Particle filter combined with the Gibbs sampling method to deal with the general sensor registration problem. The second one uses the Rao-Blackwellised Particle filter for a special case where the uncertainty of the sensor is a nearly constant measurement bias.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Online Multiple Target Tracking and Sensor Registration Using Sequential Monte Carlo Methods


    Beteiligte:
    Li, Junfeng (Autor:in) / Ng, William (Autor:in) / Godsill, Simon (Autor:in)


    Erscheinungsdatum :

    01.03.2007


    Format / Umfang :

    9049053 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch