Fast deconvolution algorithm based on compressed focus grid points
WANG Yue1， YANG Chao1， WANG Yansong1， HU Dingyu2
1.School of Mechanical and Automotive Engineering,Shanghai University of Engineering Science, Shanghai 201600,China
2.School of Urban Rail Transportation,Shanghai University of Engineering Science, Shanghai 201600,China
摘要为提升反卷积算法的计算效率，提出一种压缩聚焦网格点的快速反卷积算法。该算法基于函数波束形成的输出，根据设定的声源识别阈值，压缩参与反卷积算法循环的聚焦网格点数。算法融合了函数波束形成与CLEAN-SC（CLEAN based on spatial source coherence）的优点，可进一步提高多声源定位的空间分辨率，并有效降低算法计算时间。仿真和实验表明，所提算法对低于瑞利极限的不相干多声源具有良好的识别效果；实验中，与CLEAN-SC相比，所提算法的计算效率提升了约 3.90倍。
Abstract：In order to improve the computational efficiency of the deconvolution algorithm, a fast deconvolution algorithm was proposed to compress focus grid points. Based on the output of functional beamforming, the algorithm compressed the number of focus grid points participating in the deconvolution algorithm loop according to the set sound source recognition threshold. The algorithm combines the advantages of functional beamforming and CLEAN-SC（CLEAN based on spatial source coherence）, which can further improve the spatial resolution of multi-source location and effectively reduce the calculation time of the algorithm. Simulations and experiments show that the proposed algorithm has a good recognition effect for incoherent multiple sound sources below the Rayleigh limit. In the experiment, the computational efficiency of the proposed algorithm is about 3.90 times higher than that of CLEAN-SC.
王月1，杨超1，王岩松1，胡定玉2. 基于压缩聚焦网格点的快速反卷积算法[J]. 振动与冲击, 2022, 41(6): 250-255.
WANG Yue1， YANG Chao1， WANG Yansong1， HU Dingyu2. Fast deconvolution algorithm based on compressed focus grid points. JOURNAL OF VIBRATION AND SHOCK, 2022, 41(6): 250-255.
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