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基于稀疏贝叶斯学习的深海近海面垂直阵列宽带声源定位

Broadband source localization using near-surface vertical array in deep ocean based on sparse Bayesian learning

  • 摘要: 针对深海声影区宽带声源无源定位中传统多重谱方法存在的干涉结构不完整、深度分辨率不足以及波束形成栅瓣干扰等问题, 提出了一种基于稀疏贝叶斯学习的高分辨定位方法。首先通过射线理论建立深海声影区模型, 将接收信号的频率–角度干涉特征映射至深度–距离域; 之后将稀疏贝叶斯学习引入声源定位过程, 在抑制俯仰角栅瓣干扰的同时提升角度分辨力, 保证干涉结构的完整性; 并进一步将该方法拓展至声源深度估计问题, 实现深度维的高分辨解算。海试结果表明, 稀疏贝叶斯学习方法应用于深海宽带声源无源定位能有效实现多目标分辨定位。

     

    Abstract: This study addresses the limitations of traditional multispectral methods for passive localization of broadband acoustic sources within the deep-sea shadow zone, including incomplete interference structures, insufficient depth resolution, and beamforming grating lobe interference. A high-resolution localization method based on ​sparse Bayesian learning (SBL) is proposed. First, a model of deep-sea shadow zone is established using ray theory, which maps the frequency-angle interference characteristics of the received signals to the depth-range domain. Then, the SBL is introduced into the source localization process to suppress elevation-angle grating lobe interferences while improving angular resolution, thus ensuring the integrity of the interference structures. The method is further extended to estimate the source depth, achieving high-resolution depth-dimensional resolution. Sea trial results demonstrate that the SBL-based approach effectively resolves multi-target localization in passive monitoring of deep-sea broadband acoustic sources.

     

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