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Performance analysis of beamforming algorithm based on compressed sensing
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AI
DOI：10.1016/j.apacoust.2022.108987.png)
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Abstract
En 中文
The beamforming (BF) algorithm is widely used in sound source recognition due to its superior perfor-mance, but its main lobe is wide, side lobes are high, and its running speed is slow. Therefore, a method based on the compressed sensing beamforming method is proposed. The sound source measurement model is established, the compressed sensing reconstruction matrix is applied to the beamforming method, and propose a compressed sensing beamforming sound source recognition method. The sound source recognition performance of orthogonal matching pursuit (OMP), generalized orthogonal matching pursuit (gOMP), and regularized orthogonal matching pursuit (ROMP) is compared and analyzed through MATLAB simulation, and the OMP algorithm is selected to combine with the beamforming method. Further study the OMP-BF way and compare and analyze the recognition accuracy and running time of OMP-BF with functional beamforming (F-BF) and L1 minimum norm method beamforming (L1-BF). The results show that the OMP-BF method can accurately identify the sound source location, and the run-ning time is much lower than L1-BF and F-BF. Finally, through experiments, the effectiveness of the algo-rithm is verified. (c) 2022 Elsevier Ltd. All rights reserved.
Keywords:
Beamforming
Compressed sensing
Sound source recognition
Greedy algorithm
Orthogonal matching tracking
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3.6
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