{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T16:31:09Z","timestamp":1783528269937,"version":"3.55.0"},"reference-count":30,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2020,9,3]],"date-time":"2020-09-03T00:00:00Z","timestamp":1599091200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"BUPT Basic Research Funding","award":["500419757"],"award-info":[{"award-number":["500419757"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61901049"],"award-info":[{"award-number":["61901049"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Radio frequency interference, which makes it difficult to produce high-quality radar spectrograms, is a major issue for micro-Doppler-based human activity recognition (HAR). In this paper, we propose a deep-learning-based method to detect and cut out the interference in spectrograms. Then, we restore the spectrograms in the cut-out region. First, a fully convolutional neural network (FCN) is employed to detect and remove the interference. Then, a coarse-to-fine generative adversarial network (GAN) is proposed to restore the part of the spectrogram that is affected by the interferences. The simulated motion capture (MOCAP) spectrograms and the measured radar spectrograms with interference are used to verify the proposed method. Experimental results from both qualitative and quantitative perspectives show that the proposed method can mitigate the interference and restore high-quality radar spectrograms. Furthermore, the comparison experiments also demonstrate the efficiency of the proposed approach.<\/jats:p>","DOI":"10.3390\/s20175007","type":"journal-article","created":{"date-parts":[[2020,9,3]],"date-time":"2020-09-03T08:40:26Z","timestamp":1599122426000},"page":"5007","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":13,"title":["A Deep-Learning Method for Radar Micro-Doppler Spectrogram Restoration"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7578-8515","authenticated-orcid":false,"given":"Yuan","family":"He","sequence":"first","affiliation":[{"name":"Key Laboratory of Trustworthy Distributed Computing and Service (BUPT), Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1919-2327","authenticated-orcid":false,"given":"Xinyu","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Trustworthy Distributed Computing and Service (BUPT), Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Runlong","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Trustworthy Distributed Computing and Service (BUPT), Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianping","family":"Wang","sequence":"additional","affiliation":[{"name":"Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), Delft University of Technology, 2628CD Delft, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaojun","family":"Jing","sequence":"additional","affiliation":[{"name":"Key Laboratory of Trustworthy Distributed Computing and Service (BUPT), Beijing University of Posts and Telecommunications, Beijing 100876, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1109\/MSP.2015.2502784","article-title":"Radar Signal Processing for Elderly Fall Detection: The future for in-home monitoring","volume":"33","author":"Amin","year":"2016","journal-title":"IEEE Signal Process. 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