{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:21:39Z","timestamp":1760242899964,"version":"build-2065373602"},"reference-count":34,"publisher":"MDPI AG","issue":"11","license":[{"start":{"date-parts":[[2016,11,8]],"date-time":"2016-11-08T00:00:00Z","timestamp":1478563200000},"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":["61671122 and 61571081"],"award-info":[{"award-number":["61671122 and 61571081"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Multiple illuminators of opportunity (IOs) and a large rotation angle are often required for current passive radar imaging techniques. However, a large rotation angle demands a long observation time, which cannot be implemented for actual passive radar system. To overcome this disadvantage, this paper proposes a super-resolution passive radar imaging framework with a sparsity-inducing compressed sensing (CS) technique, which allows for fewer IOs and a smaller rotation angle. In the proposed imaging framework, the sparsity-based passive radar imaging is modeled mathematically, and the spatial frequencies and amplitudes of different scatterers on the target are recovered by the log-sum penalty function-based CS reconstruction algorithm. In doing so, a super-resolution passive radar imagery is obtained by the frequency searching approach. Simulation results not only validate that the proposed method outperforms existing super-resolution algorithms, such as ESPRIT and RELAX, especially in the cases with low signal-to-noise ratio (SNR) and limited number of measurements, but also have shown that our proposed method can perform robust reconstruction no matter if the target is on grid or not.<\/jats:p>","DOI":"10.3390\/rs8110929","type":"journal-article","created":{"date-parts":[[2016,11,8]],"date-time":"2016-11-08T09:58:26Z","timestamp":1478599106000},"page":"929","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Sparsity-Inducing Super-Resolution Passive Radar Imaging with Illuminators of Opportunity"],"prefix":"10.3390","volume":"8","author":[{"given":"Shunsheng","family":"Zhang","sequence":"first","affiliation":[{"name":"Research Institute of Electronic Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Yongqiang","family":"Zhang","sequence":"additional","affiliation":[{"name":"Research Institute of Electronic Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Wen-Qin","family":"Wang","sequence":"additional","affiliation":[{"name":"Research Institute of Electronic Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China"}]},{"given":"Cheng","family":"Hu","sequence":"additional","affiliation":[{"name":"Electronics and Information School, Beijing Institute of Technology, Beijing 100081, China"}]},{"given":"Tat","family":"Yeo","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117576, Singapore"}]}],"member":"1968","published-online":{"date-parts":[[2016,11,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1049\/ip-rsn:20045077","article-title":"FM radio based bistatic radar","volume":"152","author":"Howland","year":"2005","journal-title":"IEE P-Radar Sonor Navig."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Dilallo, A., Farina, A., Fulcoli, R., Genovesi, P., Lalli, R., and Mancinelli, R. 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