Data association which obtains relationship between radar measurements and existing tracks plays one important role in radar multiple-target tracking (MTT) systems. A new approach to data association based on the competitive Hopfield neural network (CHNN) is investigated, where the matching between radar measurements and existing target tracks is used as a criterion to achieve a global consideration. Embedded within the CHNN is a competitive learning algorithm that resolves the dilemma of occasional irrational solutions in traditional Hopfield neural networks. Additionally, it is also shown that our proposed CHNN-based network is guaranteed to converge to a stable state in performing data association and the CHNN-based data association combined with an MTT system demonstrates target tracking capability. Computer simulation results indicate that this approach successfully solves the data association problems.


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    Titel :

    Multiple-Target Tracking with Competitive Hopfield Neural Network Based Data Association


    Beteiligte:
    Yi-Nung Chung, (Autor:in) / Pao-Hua Chou, (Autor:in) / Maw-Rong Yang, (Autor:in)


    Erscheinungsdatum :

    01.07.2007


    Format / Umfang :

    3544184 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





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