ABSTRACT
The chapter presents the detailed analysis of multistatic experimental radar data in the context of human micro-Doppler for unarmed versus armed personnel classification and identification of specific individuals based on their walking gait. It discusses the experimental setups, the different types of features and classifiers, and the different approaches to use multistatic information. The chapter then describes some introductory results obtained within an indoor scenario, using a C-band frequency-modulated continuous wave (FMCW) radar, at the University of Glasgow, Scotland, to analyze the bistatic signatures of common movements such as sitting and standing, picking up objects from the floor, walking, and waving with hands. A naive Bayes (NB) classifier was applied to the features extracted to evaluate the success rate of the monostatic configuration in comparison with the bistatic result. Finally, the chapter draws conclusions and outlines some future research trends that are considered to be significant in this context.
