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Article

DOA Estimation Method for Vector Hydrophones Based on Sparse Bayesian Learning

by
Hongyan Wang
1,
Yanping Bai
1,*,
Jing Ren
1,
Peng Wang
1,
Ting Xu
1,
Wendong Zhang
2 and
Guojun Zhang
2
1
School of Mathematics, North University of China, Taiyuan 030051, China
2
State Key Laboratory of Dynamic Testing Technology, North University of China, Taiyuan 030051, China
*
Author to whom correspondence should be addressed.
Sensors 2024, 24(19), 6439; https://doi.org/10.3390/s24196439
Submission received: 7 September 2024 / Revised: 2 October 2024 / Accepted: 3 October 2024 / Published: 4 October 2024
(This article belongs to the Section Optical Sensors)

Abstract

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.
Keywords: DOA estimation; vector hydrophone; compressed sensing; sparse Bayesian learning DOA estimation; vector hydrophone; compressed sensing; sparse Bayesian learning

Share and Cite

MDPI and ACS Style

Wang, H.; Bai, Y.; Ren, J.; Wang, P.; Xu, T.; Zhang, W.; Zhang, G. DOA Estimation Method for Vector Hydrophones Based on Sparse Bayesian Learning. Sensors 2024, 24, 6439. https://doi.org/10.3390/s24196439

AMA Style

Wang H, Bai Y, Ren J, Wang P, Xu T, Zhang W, Zhang G. DOA Estimation Method for Vector Hydrophones Based on Sparse Bayesian Learning. Sensors. 2024; 24(19):6439. https://doi.org/10.3390/s24196439

Chicago/Turabian Style

Wang, Hongyan, Yanping Bai, Jing Ren, Peng Wang, Ting Xu, Wendong Zhang, and Guojun Zhang. 2024. "DOA Estimation Method for Vector Hydrophones Based on Sparse Bayesian Learning" Sensors 24, no. 19: 6439. https://doi.org/10.3390/s24196439

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