Sound Source Identification Based on Orthogonal Matching Pursuit Algorithm Guided by Source Strengthen Prior

doi:10.3901/JME.2022.24.020

Abstract

Abstract: The theory of compressed sensing provides a theoretical possibility and a way to realize high-resolution sound source identification and localization with fewer microphones. Therefore, more and more scholars apply the compressed sensing method to solve sound source identification and location problems. Among the existing compressed sensing reconstruction algorithms, orthogonal matching pursuit (OMP) algorithm has the advantages of small sidelobe, high resolution, simple algorithm process, fast calculation speed, and easy hardware implementation, which has the wide application potential. However, the OMP algorithm shows poor positioning performance for low-frequency sound source, and is prone to positioning deviation when the focus plane is densely meshed, which limits the application scope of the algorithm. For this reason, an OMP algorithm based on prior of sound source strengthen is proposed. In this method, prior information of source strengthen is introduced into the atom selection process of OMP, which can better overcome the atomic selection error caused by the high correlation between atoms of sensing matrix when the analysis frequency is low or the focus plane is densely meshed. This algorithm further improves the spatial resolution of the sound source localization and broadens the frequency range to which the algorithm is applicable. In practical applications, it can help us achieve higher accuracy of sound source localization and wide-band sound sources identification.

Key words: sound source identification, compressed sensing, OMP algorithm, sensing matrix, correlation

CLC Number:

TB532

XU Liang, QUAN Luchun, SHANG Junchao, LI Jinghao, ZHANG Xiaozheng. Sound Source Identification Based on Orthogonal Matching Pursuit Algorithm Guided by Source Strengthen Prior[J]. Journal of Mechanical Engineering, 2022, 58(24): 20-31.

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