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In practice, RATR usually needs not only to recognize in-library samples but also to reject out-of-library samples. However, most rejection methods lack a specific and accurate description of the underlying distribution of HRRP, which limits the effectiveness of the rejection task. Therefore, this paper proposes a novel rejection method for HRRP, named Deep Multi-modal Support Vector Data Description (DMMSVDD). On the one hand, it forms a more compact rejection boundary with the Gaussian mixture model in consideration of the high-dimensional and multi-modal structure of HRRP. On the other hand, it captures the global temporal information and channel-dependent information with a dual attention module to gain more discriminative structured features, which are optimized jointly with the rejection boundary. In addition, a semi-supervised extension is proposed to refine the boundary with available out-of-library samples. Experimental results based on measured data show that the proposed methods demonstrate significant improvement in the HRRP rejection performance.<\/jats:p>","DOI":"10.3390\/rs16040649","type":"journal-article","created":{"date-parts":[[2024,2,9]],"date-time":"2024-02-09T08:12:03Z","timestamp":1707466323000},"page":"649","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Radar High-Resolution Range Profile Rejection Based on Deep Multi-Modal Support Vector Data Description"],"prefix":"10.3390","volume":"16","author":[{"given":"Yue","family":"Dong","sequence":"first","affiliation":[{"name":"National Key Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9659-5608","authenticated-orcid":false,"given":"Penghui","family":"Wang","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"},{"name":"Hangzhou Institute of Technology, Xidian University, Hangzhou 311200, China"},{"name":"Institute of Information Sensing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ming","family":"Fang","sequence":"additional","affiliation":[{"name":"Shanghai Aerospace Electronic Technology Institute, Shanghai 201109, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yifan","family":"Guo","sequence":"additional","affiliation":[{"name":"Shanghai Aerospace Electronic Technology Institute, Shanghai 201109, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lili","family":"Cao","sequence":"additional","affiliation":[{"name":"Shanghai Aerospace Electronic Technology Institute, Shanghai 201109, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Junkun","family":"Yan","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"},{"name":"Hangzhou Institute of Technology, Xidian University, Hangzhou 311200, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hongwei","family":"Liu","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Radar Signal Processing, Xidian University, Xi\u2019an 710071, China"},{"name":"Hangzhou Institute of Technology, Xidian University, Hangzhou 311200, China"},{"name":"Institute of Information Sensing, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,2,9]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"190","DOI":"10.1016\/j.isprsjprs.2021.10.010","article-title":"Balance learning for ship detection from synthetic aperture radar remote sensing imagery","volume":"182","author":"Zhang","year":"2021","journal-title":"ISPRS J. 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