{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T18:31:54Z","timestamp":1770834714373,"version":"3.50.1"},"reference-count":22,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2022,7,22]],"date-time":"2022-07-22T00:00:00Z","timestamp":1658448000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62131020"],"award-info":[{"award-number":["62131020"]}]},{"name":"National Natural Science Foundation of China","award":["62001508"],"award-info":[{"award-number":["62001508"]}]},{"name":"National Natural Science Foundation of China","award":["61971434"],"award-info":[{"award-number":["61971434"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>The traditional inverse synthetic aperture radar (ISAR) imaging uses matched filtering and pulse accumulation methods. When improving the resolution and real-time performance, there are some problems, such as the high sampling rate and large amount of data. Although the compressed sensing (CS) method can realize high-resolution imaging with small sampling data, the sparse reconstruction algorithm has high computational complexity and is time-consuming. The imaging result is limited by the model and sparsity hypothesis. We propose a novel CS-ISAR imaging method using an attention generative adversarial network (AGAN). The generator of AGAN is a modified U-net consisting of both spatial and channel-wise attention. The trained generator can learn the imaging operation from down-sampling data to high-resolution ISAR images. Simulations and measured data experiments are given to validate the advantage of the proposed method.<\/jats:p>","DOI":"10.3390\/rs14153509","type":"journal-article","created":{"date-parts":[[2022,7,25]],"date-time":"2022-07-25T01:42:13Z","timestamp":1658713333000},"page":"3509","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Inverse Synthetic Aperture Radar Imaging Using an Attention Generative Adversarial Network"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4816-5675","authenticated-orcid":false,"given":"Yanxin","family":"Yuan","sequence":"first","affiliation":[{"name":"Institute 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":"Institute of Information and Navigation, Air Force Engineering University, Xi\u2019an 710077, China"},{"name":"Collaborative Innovation Center of Information Sensing and Understanding, Xi\u2019an 710077, China"},{"name":"Key Laboratory for Information Science of Electromagnetic Waves (Ministry of Education), Fudan University, Shanghai 200433, China"}]},{"given":"Jiacheng","family":"Ni","sequence":"additional","affiliation":[{"name":"Institute of Information and Navigation, Air Force Engineering University, Xi\u2019an 710077, China"},{"name":"Collaborative Innovation Center of Information Sensing and Understanding, Xi\u2019an 710077, China"}]},{"given":"Qun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Institute of Information and Navigation, Air Force Engineering University, Xi\u2019an 710077, China"},{"name":"Collaborative Innovation Center of Information Sensing and Understanding, 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":[[2022,7,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chen, V., and Martorella, M. 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