Wang Jia, Zhang Lanyue, Hu Bo, Wu Di, Hu Xueru
National Key Laboratory of Underwater Acoustic Technology, Harbin Engineering University, Harbin 150001, China.
Key Laboratory of Marine Information Acquisition and Security (Harbin Engineering University), Ministry of Industry and Information Technology, Harbin 150001, China.
J Acoust Soc Am. 2024 Jan 1;155(1):328-342. doi: 10.1121/10.0024352.
In the application of the matched field processing (MFP) algorithm for underwater acoustic source localization, the measurements at each time step are conventionally processed independently. This study incorporates the prior information about the continuous spatial changes of the source over time under realistic conditions, a factor anticipated to improve localization performance. In this paper, a sparse Bayesian learning (SBL) algorithm based on the spatio-temporal structure-aware is described. We exploit a structure prior for sparse coefficients to capture the continuous spatial structure between adjacent time steps. Moreover, the sparse coefficient can automatically select the update method, utilizing the statistical information from adjacent neighbors or updating independently. The hidden variables in the hierarchical Bayesian framework are inferred via variational Bayesian inference (VBI). Additionally, we extend the proposed method to the multi-frequency case. This method inherits the advantages of the SBL and further reduces position estimation errors. Compared to other approaches, the construction of an accurate motion model is not required. The efficacy of the proposed algorithm is demonstrated through simulation examples and an analysis of the SWellEx-96 experimental data.
在将匹配场处理（MFP）算法应用于水下声源定位时，传统上对每个时间步的测量进行独立处理。本研究纳入了在实际条件下声源随时间连续空间变化的先验信息，预计这一因素会提高定位性能。本文描述了一种基于时空结构感知的稀疏贝叶斯学习（SBL）算法。我们利用稀疏系数的结构先验来捕捉相邻时间步之间的连续空间结构。此外，稀疏系数可以自动选择更新方法，利用来自相邻邻域的统计信息或独立更新。通过变分贝叶斯推理（VBI）推断分层贝叶斯框架中的隐藏变量。此外，我们将所提出的方法扩展到多频情况。该方法继承了SBL的优点，并进一步减少了位置估计误差。与其他方法相比，不需要构建精确的运动模型。通过仿真示例和对SWellEx - 96实验数据的分析，证明了所提算法的有效性。
