{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T01:01:09Z","timestamp":1760230869577,"version":"build-2065373602"},"reference-count":39,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2022,8,15]],"date-time":"2022-08-15T00:00:00Z","timestamp":1660521600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Key Laboratory of Space Microwave Technology","award":["6142411342104","KGJ202102"],"award-info":[{"award-number":["6142411342104","KGJ202102"]}]},{"name":"National Radar Signal Processing Laboratory","award":["6142411342104","KGJ202102"],"award-info":[{"award-number":["6142411342104","KGJ202102"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>When the transmitter is in motion, the airborne passive bistatic radar (PBR) has a complex clutter geometry and lacks independent and identically distributed training samples in clutter estimation and suppression. In order to solve these problems, this paper proposes a space\u2013time adaptive processing (STAP) algorithm based on root off-grid sparse Bayesian learning. The proposed algorithm first models the space\u2013time base of the dictionary as an adjustable state. Then, the positions of those dynamic bases are optimized by iterating a maximum expectation algorithm. In this way, the off-grid error in clutter estimation can be eliminated even when the modeling grid is wide. To further improve the accuracy of clutter estimation, the proposed algorithm eliminates the error caused by samples with singular values in the root off-grid sparse Bayes learning by artificially adding pseudorandom noise and using hypothesis testing. The simulation results show that the proposed algorithm achieves better performance than the existing algorithms.<\/jats:p>","DOI":"10.3390\/rs14163963","type":"journal-article","created":{"date-parts":[[2022,8,15]],"date-time":"2022-08-15T23:44:03Z","timestamp":1660607043000},"page":"3963","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Airborne Passive Bistatic Radar Clutter Suppression Algorithm Based on Root Off-Grid Sparse Bayesian Learning"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3167-449X","authenticated-orcid":false,"given":"Jipeng","family":"Wang","sequence":"first","affiliation":[{"name":"National Laboratory of Radar Signal Processing, School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jun","family":"Wang","sequence":"additional","affiliation":[{"name":"National Laboratory of Radar Signal Processing, School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3434-6312","authenticated-orcid":false,"given":"Luo","family":"Zuo","sequence":"additional","affiliation":[{"name":"National Laboratory of Radar Signal Processing, School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuai","family":"Guo","sequence":"additional","affiliation":[{"name":"Beijing Science and Technology Leike Electronic Information Technology Co., Ltd., Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dawei","family":"Zhao","sequence":"additional","affiliation":[{"name":"National Laboratory of Radar Signal Processing, School of Electronic Engineering, Xidian University, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1109\/MAES.2018.160146","article-title":"Olsen K E. 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