Wang Hongyan, Bai Yanping, Ren Jing, Wang Peng, Xu Ting, Zhang Wendong, Zhang Guojun
School of Mathematics, North University of China, Taiyuan 030051, China.
State Key Laboratory of Dynamic Testing Technology, North University of China, Taiyuan 030051, China.
Sensors (Basel). 2024 Oct 4;24(19):6439. doi: 10.3390/s24196439.
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.
通过广泛的文献综述发现，稀疏贝叶斯学习（SBL）主要应用于传统标量水听器，很少应用于矢量水听器。本文提出了一种基于SBL的矢量水听器波达方向（DOA）估计方法（矢量-SBL）。首先，矢量水听器同时捕获声压和质点速度，能够获取多维声场信息。其次，SBL能够精确重构接收到的矢量信号，解决了低信噪比（SNR）、快照数量有限和相干源等挑战。最后，在无需事先知道源数量的情况下，实现了对多个源的精确DOA估计。仿真实验表明，与OMP、MUSIC和CBF算法相比，该方法在低SNR、小快照、多源和相干源条件下具有更高的DOA估计精度。此外，在处理间隔很近的信号源时，它表现出卓越的分辨率。
