{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:26:21Z","timestamp":1760145981932,"version":"build-2065373602"},"reference-count":30,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2024,9,8]],"date-time":"2024-09-08T00:00:00Z","timestamp":1725753600000},"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":["61761048","202101AT070132"],"award-info":[{"award-number":["61761048","202101AT070132"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Basic Research Special General project of Yunnan Province, China","award":["61761048","202101AT070132"],"award-info":[{"award-number":["61761048","202101AT070132"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>To solve the problem that the hydrophone arrays are disturbed by ocean noise when collecting signals in shallow seas, resulting in reduced accuracy and resolution of target orientation estimation, a direction-of-arrival (DOA) estimation algorithm based on iterative EMD interval thresholding (EMD-IIT) and off-grid sparse Bayesian learning is proposed. Firstly, the noisy signal acquired by the hydrophone array is denoised by the EMD-IIT algorithm. Secondly, the singular value decomposition is performed on the denoised signal, and then an off-grid sparse reconstruction model is established. Finally, the maximum a posteriori probability of the target signal is obtained by the Bayesian learning algorithm, and the DOA estimate of the target is derived to achieve the orientation estimation of the target. Simulation analysis and sea trial data results show that the algorithm achieves a resolution probability of 100% at an azimuthal separation of 8\u00b0 between adjacent signal sources. At a low signal-to-noise ratio of \u22129 dB, the resolution probability reaches 100%. Compared with the conventional MUSIC-like and OGSBI-SVD algorithms, this algorithm can effectively eliminate noise interference and provides better performance in terms of localization accuracy, algorithm runtime, and algorithm robustness.<\/jats:p>","DOI":"10.3390\/s24175835","type":"journal-article","created":{"date-parts":[[2024,9,9]],"date-time":"2024-09-09T05:06:06Z","timestamp":1725858366000},"page":"5835","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Off-Grid Underwater Acoustic Source Direction-of-Arrival Estimation Method Based on Iterative Empirical Mode Decomposition Interval Threshold"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8238-4420","authenticated-orcid":false,"given":"Chuanxi","family":"Xing","sequence":"first","affiliation":[{"name":"College of Electrical and Information Technology, Yunnan Minzu University, Kunming 650504, China"},{"name":"Yunnan Key Laboratory of Unmanned Autonomous System, Kunming 650504, China"}]},{"given":"Guangzhi","family":"Tan","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Technology, Yunnan Minzu University, Kunming 650504, China"},{"name":"Yunnan Key Laboratory of Unmanned Autonomous System, Kunming 650504, China"}]},{"given":"Saimeng","family":"Dong","sequence":"additional","affiliation":[{"name":"College of Electrical and Information Technology, Yunnan Minzu University, Kunming 650504, China"},{"name":"Yunnan Key Laboratory of Unmanned Autonomous System, Kunming 650504, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,8]]},"reference":[{"key":"ref_1","first-page":"185","article-title":"Theoretical Bases and Application Development Trend of Vector Sonar Technology","volume":"26","author":"Yang","year":"2018","journal-title":"J. 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