Improvement of orthogonal matching pursuit deconvolution beamforming method for acoustic source identification

Author:

Zhang Jinyuan1,Wen Yiyun1,Yan Jiahui2,Yang Xiaoguang2ORCID,Chu Zhigang2ORCID

Affiliation:

1. Chongqing Automotive Power System Testing Engineering Technology Research Center, Chongqing Industry Polytechnic College, Chongqing, PR China

2. College of Mechanical and Vehicle Engineering, Chongqing University, Chongqing, PR China

Abstract

The deconvolution approach for the mapping of acoustic sources (DAMAS) based on orthogonal matching pursuit (OMP-DAMAS) has attracted much attention due to its advantages of high spatial resolution and excellent capability to suppress spurious sources. In this paper, we propose an improved version of OMP-DAMAS based on fast Fourier transformation (FFT), abbreviated as FFT-OMP-DAMAS. This method assumes that the array point spread functions (PSFs) are spatially shift-invariant. The sum of the product of the acoustic source distribution and PSFs is converted into a convolution form, which is further converted into a product in the wave number domain. With these conversions, FFT can be used to improve the solving speed. Both simulations and experiments show that the proposed method not only inherits the advantages of OMP-DAMAS in terms of spatial resolution and spurious source suppression but also significantly improves the computational efficiency compared with OMP-DAMAS.

Funder

Graduate Research and Innovation Foundation of Chongqing, China

Science and Technology Research Program of Chongqing Municipal Education Commission

Publisher

SAGE Publications

Subject

Mechanical Engineering,Geophysics,Mechanics of Materials,Acoustics and Ultrasonics,Building and Construction,Civil and Structural Engineering

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Research on single-pixel imaging reconstruction algorithm based on residual attention channel network;2023 International Conference on Image Processing, Computer Vision and Machine Learning (ICICML);2023-11-03

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