Noise-Robust Classification of Ground Moving Targets Based on Time-Frequency Feature From Micro-Doppler Signature

A noise-robust classification method is proposed to discriminate the moving vehicle and walking human via the time-frequency feature extracted from the micro-Doppler signature of low resolution radar. Since the signal-to-noise ratio (SNR) directly relates to the distance between the target and radar for a given noise power and radar power, the denoising preprocessing is usually required to increase the classification distance between the target and radar in the real application. In this paper, we extend the real-valued probabilistic principal component analysis model to the complex-value domain, and develop the complex probabilistic principal component analysis (CPPCA) model for the complex-valued echoes from the ground moving targets. Then, the denoising preprocessing is accomplished based on signal reconstruction with CPPCA model, where we utilize the Bayesian inference criterion (BIC) to adaptively select the principal components. Compared with the existing CLEAN-based noise reduction method, the CPPCA-BIC-based method can work without SNR prior information. After denoising, a 3-D time-frequency feature vector is extracted from the denoised micro-Doppler signatures of the two kinds of ground targets, and the classification is performed via support vector machine classifier. In the experiments based on the measured data, the proposed classification scheme shows good classification and denoising performances under the relatively low SNR condition. The proposed method can also be applied to other classification problems based on micro-Doppler effect.