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Keywords = coherence-excluding coherence optimized orthogonal matching pursuit (CCOOMP)

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15 pages, 3341 KB  
Article
Underwater Acoustic Matched Field Imaging Based on Compressed Sensing
by Huichen Yan, Jia Xu, Teng Long and Xudong Zhang
Sensors 2015, 15(10), 25577-25591; https://doi.org/10.3390/s151025577 - 7 Oct 2015
Cited by 4 | Viewed by 5725
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 [...] Read more.
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. Full article
(This article belongs to the Section Physical Sensors)
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