{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:15:30Z","timestamp":1760242530536,"version":"build-2065373602"},"reference-count":21,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2017,9,27]],"date-time":"2017-09-27T00:00:00Z","timestamp":1506470400000},"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":["61401340","61573059","61771371"],"award-info":[{"award-number":["61401340","61573059","61771371"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Basic Research Plan in Shaanxi Province of China","award":["2016JM6035"],"award-info":[{"award-number":["2016JM6035"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Model training is a key technique for radar target recognition. Traditional model training algorithms in the framework of single task leaning ignore the relationships among multiple tasks, which degrades the recognition performance. In this paper, we propose a clustered multi-task learning, which can reveal and share the multi-task relationships for radar target recognition. To further make full use of these relationships, the latent multi-task relationships in the projection space are taken into consideration. Specifically, a constraint term in the projection space is proposed, the main idea of which is that multiple tasks within a close cluster should be close to each other in the projection space. In the proposed method, the cluster structures and multi-task relationships can be autonomously learned and utilized in both of the original and projected space. In view of the nonlinear characteristics of radar targets, the proposed method is extended to a non-linear kernel version and the corresponding non-linear multi-task solving method is proposed. Comprehensive experimental studies on simulated high-resolution range profile dataset and MSTAR SAR public database verify the superiority of the proposed method to some related algorithms.<\/jats:p>","DOI":"10.3390\/s17102218","type":"journal-article","created":{"date-parts":[[2017,9,27]],"date-time":"2017-09-27T10:52:25Z","timestamp":1506509545000},"page":"2218","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Clustered Multi-Task Learning for Automatic Radar Target Recognition"],"prefix":"10.3390","volume":"17","author":[{"given":"Cong","family":"Li","sequence":"first","affiliation":[{"name":"School of Aerospace Science and Technology, Xidian University, Xi\u2019an 710126, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weimin","family":"Bao","sequence":"additional","affiliation":[{"name":"School of Aerospace Science and Technology, Xidian University, Xi\u2019an 710126, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Luping","family":"Xu","sequence":"additional","affiliation":[{"name":"School of Aerospace Science and Technology, Xidian University, Xi\u2019an 710126, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hua","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Aerospace Science and Technology, Xidian University, Xi\u2019an 710126, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2017,9,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1725","DOI":"10.1049\/el.2016.3060","article-title":"Radar HRRP recognition based on discriminant deep autoencoders with small training data size","volume":"52","author":"Pan","year":"2016","journal-title":"Electron. 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