Hierarchical Classification of Moving Vehicles Based on Empirical Mode Decomposition of Micro-Doppler Signatures

A novel method is proposed for moving wheeled vehicle and tracked vehicle classification using micro-Doppler features from returned radar signals within short dwell time. In this method, an adaptive analysis technique called Empirical Mode Decomposition (EMD) is utilized to decompose the motion components of moving vehicles, and a hierarchical classification structure using the decomposition results of returned signals is proposed to discriminate the two kinds of vehicles. The first stage of the structure elementarily identifies the tracked vehicle data by checking the existence of its unique feature and a further classification via our proposed features based on EMD is implemented in the second stage by using Support Vector Machine (SVM) classifier. Experimental results based on the simulated data and measured data are presented, including the performance analysis for low signal-to-noise ratio (SNR) case, generalization evaluation for different target circumstances and comparison with some related methods.