| FUNDAMENTAL AREAS OF PHENOMENOLOGY(INCLUDING APPLICATIONS) |
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Source-Space Compressive Matched Field Processing for Source Localization |
| Hao-Zhong Wang**, Ning Wang, Da-Zhi Gao, Bo Gao |
College of Information Science and Technology, Ocean University of China, Qingdao 266100
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| Cite this article: |
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Hao-Zhong Wang, Ning Wang, Da-Zhi Gao et al 2016 Chin. Phys. Lett. 33 044301 |
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Abstract 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 $l_{1}-{\rm norm}$ 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.
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Received: 21 September 2015
Published: 29 April 2016
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| PACS: |
43.60.Kx
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(Matched field processing)
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43.60.Jn
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(Source localization and parameter estimation)
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43.30.Pc
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(Ocean parameter estimation by acoustical methods; remote sensing; imaging, inversion, acoustic tomography)
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43.30.Wi
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(Passive sonar systems and algorithms, matched field processing in underwater acoustics)
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