Classification of ground moving targets using bicepstrum-based features extracted from Micro-Doppler radar signatures

Abstract

In this article, a novel bicepstrum-based approach is suggested for ground moving radar target classification. Distinctive classification features were extracted from short-time backscattering bispectrum estimates of the micro-Doppler signature. Real radar data were obtained using surveillance Doppler microwave radar operating at 34 GHz. Classifier performance was studied in detail using the Gaussian Mixture Mode and Maximum Likelihood decision making rule, and the results were verified on a multilayer perceptron and Support Vector Machine. Experimental real radar measurements demonstrated that it is quite feasible to discern three classes of humans (single, two and three persons) walking in a vegetation cluttered environment using proposed bicepstrum-based classification features. Sophisticated bispectrum-based signal processing provides the extraction of new classification features in the form of phase relationships in the radar data. It provides a novel insight into moving radar target classification compared to the commonly used energy-based strategy.