Target classification by using pattern features extracted from bispectrum-based radar Doppler signatures

In this paper, a novel bicepstrum-based approach is proposed for moving radar target classification. In our study, pattern features are extracted from short-time backscattering bispectrum estimates measured by using ground surveillance Doppler radar. Classifier performance is studied by Gaussian mixture model (GMM) and maximum likelihood (ML) making decision method. Our experimental results show that is quite feasible to recognize three classes of humans (single, two and three humans) moving in vegetation clutter environment by using proposed bispectrum-based strategy. Bispectrum-based features extraction provides additional insight into moving radar target classification that is superior to common utilizing energy-based features.