Proceedings of the Advances in Materials, Machinery, Electrical Engineering (AMMEE 2017)

A Matched Field Processing Based on Compressed Sensing

Authors
Yingchun Chen, Yali Jiang, Biao Wang
Corresponding Author
Yingchun Chen
Available Online June 2017.
DOI
https://doi.org/10.2991/ammee-17.2017.50How to use a DOI?
Keywords
Underwater Acoustic Localization; Compressed Sensing; Matched Field Processing; Sparse Reconstruction
Abstract
The traditional MFP (matched field processing, MFP) methods of underwater acoustic target often have poor estimation performance or get inaccurate estimation result on the constrain of spatial sparse observation. Considering the problem, this paper proposed a new high-accuracy MFP estimation algorithm of underwater acoustic target based on compressed sensing by analyzing the space sparsity of underwater target location. The algorithm established the spatial sparse description model of underwater target, and compressed sensing the underwater target in spatial domain, then used the joint sparse reconstruction algorithm to achieve the MFP estimation of underwater acoustic target. The simulation results show that the method can increase the DOA estimation accuracy of underwater acoustic target at less array elements and less snapshots.
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This is an open access article distributed under the CC BY-NC license.

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Proceedings
Advances in Materials, Machinery, Electrical Engineering (AMMEE 2017)
Part of series
Advances in Engineering Research
Publication Date
June 2017
ISBN
978-94-6252-350-0
DOI
https://doi.org/10.2991/ammee-17.2017.50How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Yingchun Chen
AU  - Yali Jiang
AU  - Biao Wang
PY  - 2017/06
DA  - 2017/06
TI  - A Matched Field Processing Based on Compressed Sensing
BT  - Advances in Materials, Machinery, Electrical Engineering (AMMEE 2017)
PB  - Atlantis Press
UR  - https://doi.org/10.2991/ammee-17.2017.50
DO  - https://doi.org/10.2991/ammee-17.2017.50
ID  - Chen2017/06
ER  -