DOA Estimation Method for Vector Hydrophones Based on Sparse Bayesian Learning

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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.
Funding Information
  • National Natural Science Foundation of China as National Major Scientific Instruments Development Project (61927807, 51875535, 61774137, 202103021224195, 202103021223189, 202103021224212, 20210302123019, 2020-104, 2021-108, 20240011, 20221848)
  • National Natural Science Foundation of China (61927807, 51875535, 61774137, 202103021224195, 202103021223189, 202103021224212, 20210302123019, 2020-104, 2021-108, 20240011, 20221848)
  • Fundamental Research Program of Shanxi Province, China (61927807, 51875535, 61774137, 202103021224195, 202103021223189, 202103021224212, 20210302123019, 2020-104, 2021-108, 20240011, 20221848)
  • Shanxi Scholarship Council of China (61927807, 51875535, 61774137, 202103021224195, 202103021223189, 202103021224212, 20210302123019, 2020-104, 2021-108, 20240011, 20221848)
  • Fund Program for the Scientific Activities of Selected Returned Overseas Professionals in Shanxi Province (61927807, 51875535, 61774137, 202103021224195, 202103021223189, 202103021224212, 20210302123019, 2020-104, 2021-108, 20240011, 20221848)
  • The 18th Postgraduate Science and Technology Program of The North University of China (61927807, 51875535, 61774137, 202103021224195, 202103021223189, 202103021224212, 20210302123019, 2020-104, 2021-108, 20240011, 20221848)

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