{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T21:50:30Z","timestamp":1789768230924,"version":"4.0.1"},"reference-count":25,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2013,1,11]],"date-time":"2013-01-11T00:00:00Z","timestamp":1357862400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Due to the increasing complexity of electromagnetic signals, there exists a significant challenge for recognizing radar emitter signals. In this paper, a hybrid recognition approach is presented that classifies radar emitter signals by exploiting the different separability of samples. The proposed approach comprises two steps, namely the primary signal recognition and the advanced signal recognition. In the former step, a novel rough k-means classifier, which comprises three regions, i.e., certain area, rough area and uncertain area, is proposed to cluster the samples of radar emitter signals. In the latter step, the samples within the rough boundary are used to train the relevance vector machine (RVM). Then RVM is used to recognize the samples in the uncertain area; therefore, the classification accuracy is improved. Simulation results show that, for recognizing radar emitter signals, the proposed hybrid recognition approach is more accurate, and presents lower computational complexity than traditional approaches.<\/jats:p>","DOI":"10.3390\/s130100848","type":"journal-article","created":{"date-parts":[[2013,1,11]],"date-time":"2013-01-11T11:16:17Z","timestamp":1357902977000},"page":"848-864","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Hybrid Radar Emitter Recognition Based on Rough k-Means Classifier and Relevance Vector Machine"],"prefix":"10.3390","volume":"13","author":[{"given":"Zhutian","family":"Yang","sequence":"first","affiliation":[{"name":"School of Electronics and Information Technology, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhilu","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Technology, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhendong","family":"Yin","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Technology, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Taifan","family":"Quan","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Technology, Harbin Institute of Technology, Harbin 150001, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongjian","family":"Sun","sequence":"additional","affiliation":[{"name":"Department of Electronic Engineering, King's College London, Strand, London, WC2R 2LS, UK"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2013,1,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1037","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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