{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,4]],"date-time":"2025-11-04T11:16:30Z","timestamp":1762254990057,"version":"build-2065373602"},"reference-count":31,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2024,11,20]],"date-time":"2024-11-20T00:00:00Z","timestamp":1732060800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100014206","name":"National Key Laboratory Foundation","doi-asserted-by":"publisher","award":["2023-JCJQ-LB-007"],"award-info":[{"award-number":["2023-JCJQ-LB-007"]}],"id":[{"id":"10.13039\/501100014206","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Moving target detection is one of the most important tasks of radar systems. The clutter echo received by radar is usually strong and heterogeneous when the radar works in a complex terrain environment, resulting in performance degradation in moving target detection. Utilizing prior knowledge of the clutter distribution in the space\u2013time domain, this paper proposes a novel moving target detection network based on small-sample transfer learning and attention mechanism. The proposed network first utilizes offline data to train the feature extraction network and reduce the online training time. Meanwhile, the attention mechanism used for feature extraction is applied in the beam-Doppler domain to improve classification accuracy of targets. Then, a small amount of real-time data are applied to a small-sample transfer network to fine-tune the feature extraction network. Finally, the target detection can be realized by the fine-tuned network. Simulation experiments show that the proposed network can eliminate the influence of heterogeneous clutter on moving target detection, and the attention mechanism can improve clutter suppression under a low signal-to-noise ratio regime. The proposed network has a lower computational load compared to conventional neural networks, enabling its use in real-time applications on space-borne\/airborne radars.<\/jats:p>","DOI":"10.3390\/rs16224325","type":"journal-article","created":{"date-parts":[[2024,11,20]],"date-time":"2024-11-20T03:57:04Z","timestamp":1732075024000},"page":"4325","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Radar Moving Target Detection Based on Small-Sample Transfer Learning and Attention Mechanism"],"prefix":"10.3390","volume":"16","author":[{"given":"Jiang","family":"Zhu","sequence":"first","affiliation":[{"name":"National Key Laboratory of Science and Technology on Space Microwave, Xi\u2019an Institute of Space Radio Technology, Xi\u2019an 710199, China"}]},{"given":"Cai","family":"Wen","sequence":"additional","affiliation":[{"name":"School of Information Science and Technology, Northwest University, Xi\u2019an 710068, China"}]},{"given":"Chongdi","family":"Duan","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Science and Technology on Space Microwave, Xi\u2019an Institute of Space Radio Technology, Xi\u2019an 710199, China"}]},{"given":"Weiwei","family":"Wang","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Science and Technology on Space Microwave, Xi\u2019an Institute of Space Radio Technology, Xi\u2019an 710199, China"}]},{"given":"Xiaochao","family":"Yang","sequence":"additional","affiliation":[{"name":"National Key Laboratory of Science and Technology on Space Microwave, Xi\u2019an Institute of Space Radio Technology, Xi\u2019an 710199, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"532","DOI":"10.1049\/ip-rsn:20050128","article-title":"Synthetic aperture radar-moving target indicator processing of multi-channel airborne radar measurement data","volume":"153","author":"Himed","year":"2006","journal-title":"IEE Proc. 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