An Adaptive S-Method to Analyze Micro-Doppler Signals for Human Activity Classification
AbstractIn this paper, we propose the multiwindow Adaptive S-method (AS-method) distribution approach used in the time-frequency analysis for radar signals. Based on the results of orthogonal Hermite functions that have good time-frequency resolution, we vary the length of window to suppress the oscillating component caused by cross-terms. This method can bring a better compromise in the auto-terms concentration and cross-terms suppressing, which contributes to the multi-component signal separation. Finally, the effective micro signal is extracted by threshold segmentation and envelope extraction. To verify the proposed method, six states of motion are separated by a classifier of a support vector machine (SVM) trained to the extracted features. The trained SVM can detect a human subject with an accuracy of 95.4% for two cases without interference. View Full-Text
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Li, F.; Yang, C.; Xia, Y.; Ma, X.; Zhang, T.; Zhou, Z. An Adaptive S-Method to Analyze Micro-Doppler Signals for Human Activity Classification. Sensors 2017, 17, 2769.
Li F, Yang C, Xia Y, Ma X, Zhang T, Zhou Z. An Adaptive S-Method to Analyze Micro-Doppler Signals for Human Activity Classification. Sensors. 2017; 17(12):2769.Chicago/Turabian Style
Li, Fangmin; Yang, Chao; Xia, Yuqing; Ma, Xiaolin; Zhang, Tao; Zhou, Zhou. 2017. "An Adaptive S-Method to Analyze Micro-Doppler Signals for Human Activity Classification." Sensors 17, no. 12: 2769.
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