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Feature diversity for optimized human micro-doppler classification using multistatic radar. IEEE Transactions on Aerospace and Electronic Systems, 53(2), pp. 640-654. (doi:10.1109/TAES.2017.2651678) This is the author's final accepted version.There may be differences between this version and the published version. FEATURE DIVERSITY FOR OPTIMIZED HUMAN MICRO-DOPPLER CLASSIFICATION USING MULTISTATIC RADARFrancesco Fioranelli (1) , Matthew Ritchie (2) , Sevgi Zübeyde Gürbüz (3) , Hugh Griffiths (2) ( AbstractThis 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, polarisation, and frequency band. Two sets of experimental data collected with the multistatic radar system NetRAD are analysed for two separate problems, namely the classification of unarmed vs 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 behaviour, in comparison with the conventional approach of selecting the same features for all nodes.

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Abstract-Micro-Doppler radar signatures have a great potential for classifying pedestrians and animals, as well as their motion pattern, in a variety of surveillance applications. Due to the many degrees of freedom involved, real data needs to be complemented with accurate simulated radar data to successfully be able to design and test radar signal processing algorithms. In many cases, the ability to collect real data is limited by monetary and practical considerations, whereas in a simulated environment any desired scenario may be generated. Motion capture has been used in several works to simulate the human micro-Doppler signature measured by radar; however, validation of the approach has only been done based on visual comparisons of micro-Doppler signatures. This work validates and, more importantly, extends the exploitation of motion capture data not just to simulate micro-Doppler signatures but also to use the simulated signatures as a source of a priori knowledge to improve the classification performance of real radar data, especially in the case when the total amount of data is small.

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