Automatic classification of radar targets with micro-motions using entropy segmentation and time-frequency features

Micro-Doppler (mD) signatures have great potential in the radar micro-dynamic target classification. An automatic classification method for radar targets with micro-motions is proposed based on the idea of entropy and feature extraction from the spectrogram. In this method, the measurement of entropy is firstly conducted over the time-frequency distribution associated with the minimum filtering operation, the threshold discrimination and the region focusing to obtain the region of interest corresponding to mD signatures in the original spectrogram. It helps acquire the valid region in the time-frequency domain and reduce the computational burden in the following processing. Next, invariant moments and geometric characteristics of time-frequency distribution of mD signatures are extracted from the segmented spectrogram to construct mD feature vectors. A support vector machine (SVM) with decision-tree architecture is then used for multiclass micro-dynamic target classification from radar echoes. Finally, some experimental tests with simulated mD data are carried out to confirm the effectiveness of the proposed method and evaluate the performance under different conditions of signal-to-noise ratio (SNR), training set and feature vector. In addition, issues related to the improvement of classification performance are also discussed.