The performance of multitarget tracking in clutter can be improved by using higher accuracy data association algorithm and more efficient state estimation method. This paper presents a novel fixed-lag TSDA-AI smoothing algorithm, which enhances the accuracy of state estimation by using fixed-lag smoothing, and improves the probability of real “measurement-target” combination by using two-scan measurements and the associated amplitude feature in the data association method. Its efficiency has been confirmed by computer simulations.


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    Titel :

    Multitarget tracking in clutter using two-scan data association algorithm and fixed-lag smoothing


    Beteiligte:
    Wu Wei, (Autor:in)


    Erscheinungsdatum :

    01.06.2010


    Format / Umfang :

    1020738 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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