Micro-Doppler Feature Extraction Based on Time-Frequency Spectrogram for Ground Moving Targets Classification With Low-Resolution Radar

Abstract

A novel feature extraction method based on micro-Doppler signature is proposed to categorize ground moving targets into three kinds, i.e., single walking person, two people walking, and a moving wheeled vehicle. Signal models and measured data from a low-resolution radar are first analyzed to find the differences between the micro-Doppler signatures from the three kinds of considered targets. Then, such discriminative micro-Doppler signatures are represented by a 3-D feature vector extracted from the time-frequency spectrograms. In the experiments based on the measured data, the ratio of the between-class distance to the within-class distance, which is defined based on Fisher discriminant analysis, is exploited to assess the discriminative ability of the 3-D feature vector. Moreover, support vector machine classifier is utilized to evaluate the classification performance. Experimental results show that the proposed micro-Doppler features can achieve a good discriminative ability and a satisfactory classification performance.