{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,21]],"date-time":"2025-11-21T06:24:34Z","timestamp":1763706274067,"version":"build-2065373602"},"reference-count":39,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2022,3,30]],"date-time":"2022-03-30T00:00:00Z","timestamp":1648598400000},"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":["62131020","62001508","61871396"],"award-info":[{"award-number":["62131020","62001508","61871396"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Natural Science Basic Research Program of Shaanxi","award":["2020JQ-480"],"award-info":[{"award-number":["2020JQ-480"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The ground moving target (GMT) is defocused due to unknown motion parameters in synthetic aperture radar (SAR) imaging. Although the conventional Omega-K algorithm (Omega-KA) has been proven to be applicable for GMT imaging, its disadvantages are slow imaging speed, obvious sidelobe interference, and high computational complexity. To solve the above problems, a SAR-GMT imaging network is proposed based on trainable Omega-KA and sparse optimization. Specifically, we propose a two-dimensional (2-D) sparse imaging model deducted from the Omega-KA focusing process. Then, a recurrent neural network (RNN) based on an iterative optimization algorithm is built to learn the trainable parameters of Omega-KA by an off-line supervised training method, and the solving process of the sparse imaging model is mapped to each layer of the RNN. The proposed trainable Omega-KA network (Omega-KA-net) forms a new GMT imaging method that can be applied to high-quality imaging under down-sampling and a low signal to noise ratio (SNR) while saving the imaging time substantially. The experiments of simulation data and measured data demonstrate that the Omega-KA-net is superior to the conventional algorithms in terms of GMT imaging quality and time.<\/jats:p>","DOI":"10.3390\/rs14071664","type":"journal-article","created":{"date-parts":[[2022,3,30]],"date-time":"2022-03-30T21:28:39Z","timestamp":1648675719000},"page":"1664","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Omega-KA-Net: A SAR Ground Moving Target Imaging Network Based on Trainable Omega-K Algorithm and Sparse Optimization"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4413-8058","authenticated-orcid":false,"given":"Hongwei","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Information and Navigation, Air Force Engineering University, Xi\u2019an 710077, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4189-9206","authenticated-orcid":false,"given":"Jiacheng","family":"Ni","sequence":"additional","affiliation":[{"name":"School of Information and Navigation, Air Force Engineering University, Xi\u2019an 710077, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2710-4630","authenticated-orcid":false,"given":"Shichao","family":"Xiong","sequence":"additional","affiliation":[{"name":"School of Information and Navigation, Air Force Engineering University, Xi\u2019an 710077, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1460-4289","authenticated-orcid":false,"given":"Ying","family":"Luo","sequence":"additional","affiliation":[{"name":"School of Information and Navigation, Air Force Engineering University, Xi\u2019an 710077, China"},{"name":"Key Laboratory for Information Science of Electromagnetic Waves, Ministry of Education, Fudan University, Shanghai 200433, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2773-3437","authenticated-orcid":false,"given":"Qun","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Information and Navigation, Air Force Engineering University, Xi\u2019an 710077, China"},{"name":"Key Laboratory for Information Science of Electromagnetic Waves, Ministry of Education, Fudan University, Shanghai 200433, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,3,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhao, Y., Han, S., Yang, J., Zhang, L., Xu, H., and Wang, J. 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