Two contrasting approaches for tracking multiple targets in multi-beam forward-looking sonar images are considered. The first approach is based on assigning a Kalman filter to each target and managing the measurements with gating and a measurement-to-track data association technique. The second approach uses the recently developed particle implementation of the multiple-target probability hypothesis density (PHD) filter and a target state estimate-to-track data association technique. The two approaches are implemented and compared on both simulated sonar and real forward-looking sonar data obtained from an autonomous underwater vehicle (AUV) and demonstrate that the PHD filter with data association compares well with traditional approaches for multiple target tracking


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    Titel :

    Particle PHD filter multiple target tracking in sonar image


    Beteiligte:
    Jeong, T.T. (Autor:in)


    Erscheinungsdatum :

    01.01.2007


    Format / Umfang :

    1114595 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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