DOA Estimation Method for Vector Hydrophones Based on Sparse Bayesian Learning
Through extensive literature review, it has been found that sparse Bayesian learning (SBL) is mainly applied to traditional scalar hydrophones and is rarely applied to vector hydrophones. This article proposes a direction of arrival (DOA) estimation method for vector hydrophones based on SBL (Vector...
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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/24/19/6439 |
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| author | Hongyan Wang Yanping Bai Jing Ren Peng Wang Ting Xu Wendong Zhang Guojun Zhang |
| author_facet | Hongyan Wang Yanping Bai Jing Ren Peng Wang Ting Xu Wendong Zhang Guojun Zhang |
| author_sort | Hongyan Wang |
| collection | DOAJ |
| description | Through extensive literature review, it has been found that sparse Bayesian learning (SBL) is mainly applied to traditional scalar hydrophones and is rarely applied to vector hydrophones. This article proposes a direction of arrival (DOA) estimation method for vector hydrophones based on SBL (Vector-SBL). Firstly, vector hydrophones capture both sound pressure and particle velocity, enabling the acquisition of multidimensional sound field information. Secondly, SBL accurately reconstructs the received vector signal, addressing challenges like low signal-to-noise ratio (SNR), limited snapshots, and coherent sources. Finally, precise DOA estimation is achieved for multiple sources without prior knowledge of their number. Simulation experiments have shown that compared with the OMP, MUSIC, and CBF algorithms, the proposed method exhibits higher DOA estimation accuracy under conditions of low SNR, small snapshots, multiple sources, and coherent sources. Furthermore, it demonstrates superior resolution when dealing with closely spaced signal sources. |
| format | Article |
| id | doaj-art-ca514a7a088e4c1dae436c231ec4175e |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-ca514a7a088e4c1dae436c231ec4175e2025-08-20T01:47:37ZengMDPI AGSensors1424-82202024-10-012419643910.3390/s24196439DOA Estimation Method for Vector Hydrophones Based on Sparse Bayesian LearningHongyan Wang0Yanping Bai1Jing Ren2Peng Wang3Ting Xu4Wendong Zhang5Guojun Zhang6School of Mathematics, North University of China, Taiyuan 030051, ChinaSchool of Mathematics, North University of China, Taiyuan 030051, ChinaSchool of Mathematics, North University of China, Taiyuan 030051, ChinaSchool of Mathematics, North University of China, Taiyuan 030051, ChinaSchool of Mathematics, North University of China, Taiyuan 030051, ChinaState Key Laboratory of Dynamic Testing Technology, North University of China, Taiyuan 030051, ChinaState Key Laboratory of Dynamic Testing Technology, North University of China, Taiyuan 030051, ChinaThrough extensive literature review, it has been found that sparse Bayesian learning (SBL) is mainly applied to traditional scalar hydrophones and is rarely applied to vector hydrophones. This article proposes a direction of arrival (DOA) estimation method for vector hydrophones based on SBL (Vector-SBL). Firstly, vector hydrophones capture both sound pressure and particle velocity, enabling the acquisition of multidimensional sound field information. Secondly, SBL accurately reconstructs the received vector signal, addressing challenges like low signal-to-noise ratio (SNR), limited snapshots, and coherent sources. Finally, precise DOA estimation is achieved for multiple sources without prior knowledge of their number. Simulation experiments have shown that compared with the OMP, MUSIC, and CBF algorithms, the proposed method exhibits higher DOA estimation accuracy under conditions of low SNR, small snapshots, multiple sources, and coherent sources. Furthermore, it demonstrates superior resolution when dealing with closely spaced signal sources.https://www.mdpi.com/1424-8220/24/19/6439DOA estimationvector hydrophonecompressed sensingsparse Bayesian learning |
| spellingShingle | Hongyan Wang Yanping Bai Jing Ren Peng Wang Ting Xu Wendong Zhang Guojun Zhang DOA Estimation Method for Vector Hydrophones Based on Sparse Bayesian Learning Sensors DOA estimation vector hydrophone compressed sensing sparse Bayesian learning |
| title | DOA Estimation Method for Vector Hydrophones Based on Sparse Bayesian Learning |
| title_full | DOA Estimation Method for Vector Hydrophones Based on Sparse Bayesian Learning |
| title_fullStr | DOA Estimation Method for Vector Hydrophones Based on Sparse Bayesian Learning |
| title_full_unstemmed | DOA Estimation Method for Vector Hydrophones Based on Sparse Bayesian Learning |
| title_short | DOA Estimation Method for Vector Hydrophones Based on Sparse Bayesian Learning |
| title_sort | doa estimation method for vector hydrophones based on sparse bayesian learning |
| topic | DOA estimation vector hydrophone compressed sensing sparse Bayesian learning |
| url | https://www.mdpi.com/1424-8220/24/19/6439 |
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