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Block sparse Bayesian learning for broadband mode extraction in shallow water from a vertical array.
The Journal of the Acoustical Society of America ( IF 2.4 ) Pub Date : 2020-06-01 , DOI: 10.1121/10.0001322
Haiqiang Niu 1 , Peter Gerstoft 2 , Emma Ozanich 2 , Zhenglin Li 1 , Renhe Zhang 1 , Zaixiao Gong 1 , Haibin Wang 1
Affiliation  

The horizontal wavenumbers and modal depth functions are estimated by block sparse Bayesian learning (BSBL) for broadband signals received by a vertical line array in shallow-water waveguides. The dictionary matrix consists of multi-frequency modal depth functions derived from shooting methods given a large set of hypothetical horizontal wavenumbers. The dispersion relation for multi-frequency horizontal wavenumbers is also taken into account to generate the dictionary. In this dictionary, only a few of the entries are used to describe the pressure field. These entries represent the modal depth functions and associated wavenumbers. With the constraint of block sparsity, the BSBL approach is shown to retrieve the horizontal wavenumbers and corresponding modal depth functions with high precision, while a priori knowledge of sea bottom, moving source, and source locations is not needed. The performance is demonstrated by simulations and experimental data.

中文翻译:

块稀疏贝叶斯学习用于从垂直阵列中提取浅水中的宽带模式。

水平波数和模态深度函数通过块稀疏贝叶斯学习(BSBL)估计,用于浅水波导中垂直线阵列接收的宽带信号。字典矩阵由多频率模态深度函数组成,这些函数是根据给定大量假设水平波数的射击方法得出的。还考虑了多频水平波数的色散关系以生成字典。在此词典中,仅使用少数条目来描述压力场。这些条目表示模态深度函数和关联的波数。在块稀疏性的约束下,示出了BSBL方法以高精度检索水平波数和相应的模态深度函数,而先验地不需要了解海底,移动源和源位置。仿真和实验数据证明了该性能。
更新日期:2020-06-01
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