Source-Space Compressive Matched Field Processing for Source Localization

被引：0

作者：

王好忠 ^{[1
]}

王宁 ^{[1
]}

高大治 ^{[1
]}

高博 ^{[1
]}

机构：

[1] College of Information Science and Technology,Ocean University of China

来源：

Chinese Physics Letters | 2016年 / 04期

基金：

中国国家自然科学基金; 中央高校基本科研业务费专项资金资助;

关键词：

of; SSC; MFP; in; Source-Space Compressive Matched Field Processing for Source Localization; for; that; is;

D O I：

暂无

中图分类号：

TP311.13 []; P733.2 [海洋声学];

学科分类号：

082403 ; 1201 ;

摘要：

Source localization by matched-field processing（MFP） can be accelerated by building a database of Green’s functions which however requires a bulk-storage memory.According to the sparsity of the source locations in the search grids of MFP,compressed sensing inspires an approach to reduce the database by introducing a sensing matrix to compress the database.Compressed sensing is further used to estimate the source locations with higher resolution by solving the l1-norm optimization problem of the compressed Green’s function and the data received by a vertical/horizontal line array.The method is validated by simulation and is verified with the experimental data.

引用

页码：64 / 67

页数：4

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