{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,14]],"date-time":"2026-07-14T22:22:40Z","timestamp":1784067760312,"version":"3.55.0"},"reference-count":27,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2021,11,10]],"date-time":"2021-11-10T00:00:00Z","timestamp":1636502400000},"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":["61725105"],"award-info":[{"award-number":["61725105"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>With the development of signal processing technology and the use of new radar systems, signal aliasing and electronic interference have occurred in space. The electromagnetic signals have become extremely complicated in their current applications in space, causing difficult problems in terms of accurately identifying radar-modulated signals in low signal-to-noise ratio (SNR) environments. To address this problem, in this paper, we propose an intelligent recognition method that combines time\u2013frequency (T\u2013F) analysis and a deep neural network to identify radar modulation signals. The T\u2013F analysis of the complex Morlet wavelet transform (CMWT) method is used to extract the characteristics of signals and obtain the T\u2013F images. Adaptive filtering and morphological processing are used in T\u2013F image enhancement to reduce the interference of noise on signal characteristics. A deep neural network with the channel-separable ResNet (Sep-ResNet) is used to classify enhanced T\u2013F images. The proposed method completes high-accuracy intelligent recognition of radar-modulated signals in a low-SNR environment. When the SNR is \u221210 dB, the probability of successful recognition (PSR) is 93.44%.<\/jats:p>","DOI":"10.3390\/s21227474","type":"journal-article","created":{"date-parts":[[2021,11,11]],"date-time":"2021-11-11T23:04:46Z","timestamp":1636671886000},"page":"7474","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Radar Signal Modulation Recognition Based on Sep-ResNet"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-2226-2365","authenticated-orcid":false,"given":"Yongjiang","family":"Mao","sequence":"first","affiliation":[{"name":"Key Laboratory of Network Information System Technology, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Wenjuan","family":"Ren","sequence":"additional","affiliation":[{"name":"Key Laboratory of Network Information System Technology, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhanpeng","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Network Information System Technology, Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China"},{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100190, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,10]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1262","DOI":"10.1109\/TAES.2010.5545188","article-title":"Fast Learning of Grammar Production Probabilities in Radar Electronic Support","volume":"46","author":"Latombe","year":"2010","journal-title":"IEEE Trans. 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