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Open AccessArticle

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

1
Department of Mathematics and Computer Science, Changsha University, Changsha 410022, China
2
School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China
*
Author to whom correspondence should be addressed.
Sensors 2017, 17(12), 2769; https://doi.org/10.3390/s17122769
Received: 19 October 2017 / Revised: 20 November 2017 / Accepted: 27 November 2017 / Published: 29 November 2017
(This article belongs to the Section Physical Sensors)
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. View Full-Text
Keywords: micro-doppler signal; radar; activity classification; time-frequency analysis; support vector machine (SVM) micro-doppler signal; radar; activity classification; time-frequency analysis; support vector machine (SVM)
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MDPI and ACS Style

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.

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