{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T15:14:16Z","timestamp":1769008456791,"version":"3.49.0"},"reference-count":53,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,18]],"date-time":"2023-01-18T00:00:00Z","timestamp":1674000000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003626","name":"Defense Acquisition Program Administration","doi-asserted-by":"publisher","award":["UD200045CD"],"award-info":[{"award-number":["UD200045CD"]}],"id":[{"id":"10.13039\/501100003626","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>For real-time target classification, a study was conducted to improve the AI-based target classification performance using RCS measurements that are vulnerable to noise, but can be obtained quickly. To compensate for the shortcomings of the RCS, a 1D CNN\u2013GRU network with strengths in feature extraction and time-series processing was considered. The 1D CNN\u2013GRU was experimentally changed and designed to fit the RCS characteristics. The performance of the proposed 1D CNN\u2013GRU was compared and analyzed using the 1D CNN and 1D CNN\u2013LSTM. The designed 1D CNN\u2013GRU had the best classification performance with a high accuracy of 99.50% in complex situations, such as with different missile shapes with the same trajectory and with the same missile shapes that had the same trajectory. In addition, to confirm the general target classification performance for the RCS, a new class was verified. The 1D CNN\u2013GRU had the highest classification performance at 99.40%. Finally, as a result of comparing three networks by adding noise to compensate for the shortcomings of the RCS, the 1D CNN\u2013GRU, which was optimized for both the data set used in this paper and the newly constructed data set, was the most robust to noise.<\/jats:p>","DOI":"10.3390\/rs15030577","type":"journal-article","created":{"date-parts":[[2023,1,19]],"date-time":"2023-01-19T05:06:14Z","timestamp":1674104774000},"page":"577","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["The Design of the 1D CNN\u2013GRU Network Based on the RCS for Classification of Multiclass Missiles"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0438-7199","authenticated-orcid":false,"given":"A Ran","family":"Kim","sequence":"first","affiliation":[{"name":"Department of Mechanical Engineering, Kunsan National University, Gunsan 54150, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2480-329X","authenticated-orcid":false,"given":"Ha Seon","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Mechanical Engineering, Kunsan National University, Gunsan 54150, Republic of Korea"}]},{"given":"Chang Ho","family":"Kang","sequence":"additional","affiliation":[{"name":"Department of Mechanical System Engineering (Department of Aeronautics, Mechanical and Electronic Convergence Engineering), Kumoh National Institute of Technology, Gumi 39177, Republic of Korea"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6189-0268","authenticated-orcid":false,"given":"Sun Young","family":"Kim","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Kunsan National University, Gunsan 54150, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gal\u00e1n, J.J., Carrasco, R.A., and LaTorre, A. 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