A highly efficient compressed sensing algorithm for acoustic imaging in low signal-to-noise ratio environments

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 l1-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 l1-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 KT 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.