{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T14:24:00Z","timestamp":1775139840538,"version":"3.50.1"},"reference-count":46,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2022,11,19]],"date-time":"2022-11-19T00:00:00Z","timestamp":1668816000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Basic Research Program of Shaanxi","award":["2021-JQ-361"],"award-info":[{"award-number":["2021-JQ-361"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Radar cross section (RCS) sequences, an easy-to-obtain target feature with small data volume, play a significant role in radar target classification. However, radar target classification based on RCS sequences has the shortcomings of limited information and low recognition accuracy. In order to overcome the shortcomings of RCS-based methods, this paper proposes a spatial micro-motion target classification method based on RCS sequences encoding and convolutional neural network (CNN). First, we establish the micro-motion models of spatial targets, including precession, swing and rolling. Second, we introduce three approaches for encoding RCS sequences as images. These three types of images are Gramian angular field (GAF), Markov transition field (MTF) and recurrence plot (RP). Third, a multi-scale CNN is developed to classify those RCS feature maps. Finally, the experimental results demonstrate that RP is best at reflecting the characteristics of the target among those three encoding methods. Moreover, the proposed network outperforms other existing networks with the highest classification accuracy.<\/jats:p>","DOI":"10.3390\/rs14225863","type":"journal-article","created":{"date-parts":[[2022,11,21]],"date-time":"2022-11-21T04:33:32Z","timestamp":1669005212000},"page":"5863","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Classification of Radar Targets with Micro-Motion Based on RCS Sequences Encoding and Convolutional Neural Network"],"prefix":"10.3390","volume":"14","author":[{"given":"Xuguang","family":"Xu","sequence":"first","affiliation":[{"name":"Air and Missile Defense College, Air Force Engineering University, Xi\u2019an 710038, China"}]},{"given":"Cunqian","family":"Feng","sequence":"additional","affiliation":[{"name":"Air and Missile Defense College, Air Force Engineering University, Xi\u2019an 710038, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0449-1957","authenticated-orcid":false,"given":"Lixun","family":"Han","sequence":"additional","affiliation":[{"name":"Air and Missile Defense College, Air Force Engineering University, Xi\u2019an 710038, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,11,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.1049\/iet-rsn.2015.0547","article-title":"Current Research in Micro-Doppler: Editorial for the Special Issue on Micro-Doppler","volume":"9","author":"Tahmoush","year":"2015","journal-title":"IET Radar Sonar Nav."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"468","DOI":"10.5515\/KJKIEES.2020.31.5.468","article-title":"Effective Discrimination between Warhead and Decoy in Mid-Course Phase of Ballistic Missile","volume":"31","author":"Choi","year":"2020","journal-title":"J. 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