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Sensors 2015, 15(10), 25577-25591; doi:10.3390/s151025577

Underwater Acoustic Matched Field Imaging Based on Compressed Sensing

1
Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
2
School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China
*
Author to whom correspondence should be addressed.
Academic Editor: Piervincenzo Rizzo
Received: 4 August 2015 / Revised: 6 September 2015 / Accepted: 18 September 2015 / Published: 7 October 2015
(This article belongs to the Section Physical Sensors)
View Full-Text   |   Download PDF [3341 KB, uploaded 7 October 2015]   |  

Abstract

Matched field processing (MFP) is an effective method for underwater target imaging and localizing, but its performance is not guaranteed due to the nonuniqueness and instability problems caused by the underdetermined essence of MFP. By exploiting the sparsity of the targets in an imaging area, this paper proposes a compressive sensing MFP (CS-MFP) model from wave propagation theory by using randomly deployed sensors. In addition, the model’s recovery performance is investigated by exploring the lower bounds of the coherence parameter of the CS dictionary. Furthermore, this paper analyzes the robustness of CS-MFP with respect to the displacement of the sensors. Subsequently, a coherence-excluding coherence optimized orthogonal matching pursuit (CCOOMP) algorithm is proposed to overcome the high coherent dictionary problem in special cases. Finally, some numerical experiments are provided to demonstrate the effectiveness of the proposed CS-MFP method. View Full-Text
Keywords: matched field processing (MFP); compressed sensing (CS); wave propagation; coherence parameter; coherence-excluding coherence optimized orthogonal matching pursuit (CCOOMP) matched field processing (MFP); compressed sensing (CS); wave propagation; coherence parameter; coherence-excluding coherence optimized orthogonal matching pursuit (CCOOMP)
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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Yan, H.; Xu, J.; Long, T.; Zhang, X. Underwater Acoustic Matched Field Imaging Based on Compressed Sensing. Sensors 2015, 15, 25577-25591.

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