This research considers simulated laser radar (LADAR) vibrometry for vehicle identification. Time sampled data is considered for developing multiple nonlinear autoregressive neural network (NARNet) classifier models. Emphasis is placed on robustness to sensor location and using small amounts of data. Decision level fusion is used to combine results from multiple classifiers. Results offer improved classification performance as compared to the literature.


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    Titel :

    Vibrometry-based vehicle identification framework using nonlinear autoregressive neural networks and decision fusion


    Beteiligte:
    Ward, Marc R. (Autor:in) / Bihl, Trevor J. (Autor:in) / Bauer, Kenneth W. (Autor:in)


    Erscheinungsdatum :

    01.06.2014


    Format / Umfang :

    705532 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



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