An Adaptive S-Method to Analyze Micro-Doppler Signals for Human Activity Classification

• Published in: Sensors (Basel, Switzerland) Vol. 17; no. 12; p. 2769
• Main Authors: Li, Fangmin, Yang, Chao, Xia, Yuqing, Ma, Xiaolin, Zhang, Tao, Zhou, Zhou
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 29.11.2017; MDPI
• Subjects: Accuracy; activity classification; Algorithms; Classification; Fourier transforms; Human Activities; Human body; Human mechanics; Humans; micro-doppler signal; Principal components analysis; radar; Sensors; Support Vector Machine; support vector machine (SVM); Support vector machines; time-frequency analysis; micro-doppler signal; radar; activity classification; time-frequency analysis; support vector machine (SVM)
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s17122769

In 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.