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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    Title :

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


    Contributors:


    Publication date :

    2014-06-01


    Size :

    705532 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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