Multistatic human micro-Doppler classification of armed/unarmed personnel

Classification of different human activities using multistatic micro-Doppler data and features is considered in this study, focusing on the distinction between unarmed and potentially armed personnel. A database of real radar data with more than 550 recordings from 7 different human subjects has been collected in a series of experiments in the field with a multistatic radar system. Four key features were extracted from the micro-Doppler signature after a short time Fourier transform analysis. The resulting feature vectors were then used as individual, pairs, triplets and all together before inputting to different types of classifiers based on the discriminant analysis method. The performance of different classifiers and different feature combinations is discussed aiming at identifying the most appropriate features for the unarmed against armed personnel classification, as well as the benefit of combining multistatic data rather than using monostatic data only.