稀疏贝叶斯学习远近场混合源离网定位算法  

A sparse Bayesian learning based off-grid localization algorithm for mixed far-field and near-field sources

作　　者：李晨牧 邱龙皓 王晋晋 梁国龙[1,3,4] 沈同圣 LI Chenmu;QIU Longhao;WANG Jinjin;LIANG Guolong;SHEN Tongsheng(Acoustic Science and Technology Laboratory,Harbin Engineering University Harbin 150001;Advanced Interdisciplinary Technology Research Center,National Innovation Institute of Defense Technology Beijing 100071;Key Laboratory of Marine Information Acquisition and Security(Harbin Engineering University),Ministry of Industry and Information Technology Harbin 150001;College of Underwater Acoustic Engineering,Harbin Engineering University Harbin 150001)

机构地区：[1]哈尔滨工程大学水声技术重点实验室,哈尔滨150001 [2]国防科技创新研究院前沿交叉技术研究中心,北京100071 [3]海洋信息获取与安全工信部重点实验室(哈尔滨工程大学),哈尔滨150001 [4]哈尔滨工程大学水声工程学院,哈尔滨150001

出　　处：《声学学报》2025年第2期456-474,共19页Acta Acustica

基　　金：国家自然科学基金项目(62101153);重点实验室稳定支持项目(JCKYS2021604SSJS003)资助。

摘　　要：水下远近场混合源定位算法的定位精度往往受到采样网格的限制,粗糙的网格在降低精度的同时可能导致近场声信号功率泄露至远场,恶化远场测向结果;细密的网格使算法计算复杂度剧增,影响算法的计算效率与实用性。为此提出了一种稀疏贝叶斯学习远近场混合源离网定位算法。该算法通过建立水下声信号远近场离网模型,利用稀疏贝叶斯学习过程实现离网误差的估计与补偿,从而突破网格限制,获得更高精度的定位结果。在此基础上,还提出了一种提高计算效率的网格演化方法,该方法根据离网误差估计结果引导网格点在声源位置附近细化,实现了网格点有侧重、非均匀地覆盖感兴趣空域,有效降低了算法计算复杂度。仿真和湖试数据处理结果表明,与现有稀疏贝叶斯学习远近场混合源定位算法相比,所提算法具有更高的定位精度、分辨率和计算效率。Source localization accuracy is often limited by the sampling grid.On the one hand,a coarse sampling grid has a larger modelling error,which can lead to energy leakage from the near-field to the far-field,and the leaked energy can mask the weak farfield target signal,making it difficult to estimate the direction of arrival.On the other hand,a dense sampling grid leads to high computational complexity,making the localization algorithms computationally inefficient.In order to overcome this problem,a sparse Bayesian learning based off-grid localization algorithm for mixed far-and near-field sources is proposed.An off-grid model for mixed far-and near-field acoustic sources is constructed,then the sparse Bayesian learning method is used to estimate and compensate the off-grid error,so as to overcome the grid limitation and achieve higher localization accuracy.On this basis,a grid evolution method is developed to improve the computational efficiency,which causes the grids to cover the space of interest nonuniformly,making finer grids around the position of the acoustic sources,thereby reducing the computational complexity while retaining reasonable accuracy.Numerical simulations and experimental results show that the proposed methods achieve higher localization accuracy,resolution and computational efficiency compared to the existing sparse Bayesian learning-based source localization algorithm for far-and near-field sources.

关 键 词：声源定位 稀疏贝叶斯学习 离网模型 网格演化 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]