Robust Acoustic Imaging Based on Bregman Iteration and Fast Iterative Shrinkage-Thresholding Algorithm

The acoustic imaging (AI) technique could map the position and the strength of the sound source via the signal processing of the microphone array. Conventional methods, including far-field beamforming (BF) and near-field acoustic holography (NAH), are limited to the frequency range of measured objec...

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Bibliographic Details
Published in:Sensors (Basel, Switzerland) Vol. 20; no. 24; p. 7298
Main Authors: Huang, Linsen, Song, Shaoyu, Xu, Zhongming, Zhang, Zhifei, He, Yansong
Format: Journal Article
Language:English
Published: Switzerland MDPI AG 18.12.2020
MDPI
Subjects:
acoustic imaging
Acoustics
Algorithms
beamforming
Bregman iteration method
Decomposition
Fourier transforms
near-field acoustic holography
Noise
Regularization methods
Signal processing
sparse representation
Sparsity
acoustic imaging
near-field acoustic holography
Bregman iteration method
beamforming
sparse representation
ISSN:1424-8220, 1424-8220
Online Access:Get full text
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Summary:The acoustic imaging (AI) technique could map the position and the strength of the sound source via the signal processing of the microphone array. Conventional methods, including far-field beamforming (BF) and near-field acoustic holography (NAH), are limited to the frequency range of measured objects. A method called Bregman iteration based acoustic imaging (BI-AI) is proposed to enhance the performance of the two-dimensional acoustic imaging in the far-field and near-field measurements. For the large-scale ℓ1 norm problem, Bregman iteration (BI) acquires the sparse solution; the fast iterative shrinkage-thresholding algorithm (FISTA) solves each sub-problem. The interpolating wavelet method extracts the information about sources and refines the computational grid to underpin BI-AI in the low-frequency range. The capabilities of the proposed method were validated by the comparison between some tried-and-tested methods processing simulated and experimental data. The results showed that BI-AI separates the coherent sources well in the low-frequency range compared with wideband acoustical holography (WBH); BI-AI estimates better strength and reduces the width of main lobe compared with ℓ1 generalized inverse beamforming (ℓ1-GIB).
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ISSN:1424-8220
1424-8220
DOI:10.3390/s20247298