This paper investigates the feasibility of a mesh network of proximity sensors to track multiple targets. In such a network, the sensors report a detection when a target is within the proximity; otherwise, the sensors report no detection. Previous work has revealed the potential of target localization and tracking for a single target using these binary reports. This work introduces a particle-based probability hypothesis density (PHD) filter that is able to track multiple targets using the binary reports from a proximity sensor network. Furthermore, this work modifies another particle-based multitarget tracker for proximity sensors, namely the ClusterTrack, from 1-D tracking to 2-D. The simulations demonstrate that the PHD is able to outperform the Cluster- Track in terms of both accuracy of localization and estimating the number of targets.


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    Titel :

    Target tracking using proximity binary sensors


    Beteiligte:
    Qiang Le, (Autor:in) / Kaplan, Lance M (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.03.2011


    Format / Umfang :

    373753 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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