Journal
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
Volume 112, Issue -, Pages 113-128Publisher
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ymssp.2018.04.028
Keywords
Compressed sensing; Microphone array; Acoustic imaging; Singular value decomposition; Highly efficient
Categories
Funding
- NSFC [51375385, 51675425]
- Natural Science Basic Research Plan in Shaanxi Province of China [2016JZ013]
- Seed Foundation of Innovation and Creation for Graduate Students in Northwestern Polytechnical University [Z2016077]
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We study the acoustic imaging in low signal-to-noise ratio (SNR) environments with compressed sensing (CS) and microphone arrays. In this work, we propose an OMP-SVD method which combines the orthogonal matching pursuit (OMP) method of CS and the singular value decomposition (SVD). The performance of the proposed OMP-SVD method is compared with the CBF method, the OMP method and the l(1)-SVD method. In terms of the CPU time, the proposed method is highly efficient like the CBF method and the OMP method, and much more efficient than the l(1)-SVD method. In terms of the accuracy of the source maps, the OMP-SVD method can locate the sources exactly for the SNR as low as -10 dB and the frequency as low as 2000 Hz, while the other three different methods can only locate the sources when the SNR is greater than or equal to 5 dB. In addition, we find that the proposed method can obtain good performance when the target sparsity K-T is overestimated and there is basis mismatch. Finally, a gas leakage experiment was conducted to verify the performance of the OMP-SVD method in practical application. The results show that the OMP-SVD method is robust in low SNR environments. (C) 2018 Elsevier Ltd. All rights reserved.
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