This paper investigates the selection of different combinations of features at different multistatic radar nodes, depending on scenario parameters, such as aspect angle to the target and signal-to-noise ratio, and radar parameters, such as dwell time, polarization, and frequency band. Two sets of experimental data collected with the multistatic radar system NetRAD are analyzed for two separate problems, namely the classification of unarmed versus potentially armed multiple personnel, and the personnel recognition of individuals based on walking gait. The results show that the overall classification accuracy can be significantly improved by taking into account feature diversity at each radar node depending on the environmental parameters and target behavior, in comparison with the conventional approach of selecting the same features for all nodes.


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    Title :

    Feature Diversity for Optimized Human Micro-Doppler Classification Using Multistatic Radar




    Publication date :

    2017-04-01


    Size :

    1096523 byte




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English






    Multistatic and MIMO radar

    British Library Conference Proceedings | 2010