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

作者：Li, Fangmin; Yang, Chao^*; Xia, Yuqing; Ma, Xiaolin; Zhang, Tao; Zhou, Zhou

来源：Sensors (Switzerland), 2017, 17(12): 2769.

DOI：10.3390/s17122769

• micro-doppler signal
• radar
• activity classification
• time-frequency analysis
• support vector machine (SVM)

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.

• 出版日期2017-12
• 单位武汉理工大学; 长沙学院