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Different from bistatic inverse synthetic aperture radar (Bi-ISAR) imaging, bistatic radar coincidence imaging (Bi-RCI) provides a new tack on the bistatic radar imaging technique. In this paper, a Bi-RCI based on multiple measurement vectors (MMV) for rotating cone-shaped targets is proposed to realize Bi-RCI coherent processing and improve imaging performance. Based on the mixed mode signals, a MMV parametric model is established and measurement number coarse selection is proposed. Finally, a modified sparse Bayesian learning (MSBL) algorithm is introduced to reconstruct the target image. Simulation results demonstrate the validity and the superiority of the proposed method.<\/jats:p>","DOI":"10.1155\/2020\/3878525","type":"journal-article","created":{"date-parts":[[2020,8,13]],"date-time":"2020-08-13T23:35:05Z","timestamp":1597361705000},"page":"1-10","source":"Crossref","is-referenced-by-count":0,"title":["Bistatic Radar Coincidence Imaging Based on Multiple Measurement Vectors for Rotating Cone-Shaped Targets"],"prefix":"10.1155","volume":"2020","author":[{"given":"Rui","family":"Li","sequence":"first","affiliation":[{"name":"Institute of Information and Navigation, Air Force Engineering University, Xi\u2019an 710077, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1460-4289","authenticated-orcid":true,"given":"Ying","family":"Luo","sequence":"additional","affiliation":[{"name":"Institute of Information and Navigation, Air Force Engineering University, Xi\u2019an 710077, China"},{"name":"Key Laboratory for 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