{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:57:29Z","timestamp":1760245049171,"version":"build-2065373602"},"reference-count":22,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2017,11,29]],"date-time":"2017-11-29T00:00:00Z","timestamp":1511913600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61373042","61502361"],"award-info":[{"award-number":["61373042","61502361"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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.<\/jats:p>","DOI":"10.3390\/s17122769","type":"journal-article","created":{"date-parts":[[2017,11,30]],"date-time":"2017-11-30T03:15:15Z","timestamp":1512011715000},"page":"2769","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":12,"title":["An Adaptive S-Method to Analyze Micro-Doppler Signals for Human Activity Classification"],"prefix":"10.3390","volume":"17","author":[{"given":"Fangmin","family":"Li","sequence":"first","affiliation":[{"name":"Department of Mathematics and Computer Science, Changsha University, Changsha 410022, China"},{"name":"School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China"}]},{"given":"Chao","family":"Yang","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science, Changsha University, Changsha 410022, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8612-1756","authenticated-orcid":false,"given":"Yuqing","family":"Xia","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China"}]},{"given":"Xiaolin","family":"Ma","sequence":"additional","affiliation":[{"name":"School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China"}]},{"given":"Tao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science, Changsha University, Changsha 410022, China"}]},{"given":"Zhou","family":"Zhou","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Computer Science, Changsha University, Changsha 410022, China"}]}],"member":"1968","published-online":{"date-parts":[[2017,11,29]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"142","DOI":"10.3758\/BF03337021","article-title":"Recognizing friends by their walk: Gait perception without familiarity cues","volume":"9","author":"Cutting","year":"1977","journal-title":"Bull. 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