Improvement of Fourier-based fast iterative shrinkage-thresholding deconvolution algorithm for acoustic source identification

Chu, Zhigang; Chen, Caihui; Yang, Yang; Shen, Linbang; Chen, Xi

doi:10.1016/j.apacoust.2017.03.010

Cited by 15 publications

(6 citation statements)

References 12 publications

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“…As the acoustic source deviates from the center of the plane, the imaging results will deteriorate. To improve this problem, an irregular focus point grid proposed in [ 37 ] is used instead of the traditional regular one.…”

Section: The Design Of Acoustic Localization Sensormentioning

confidence: 99%

An Acoustic Localization Sensor Based on MEMS Microphone Array for Partial Discharge

Yan

Chen

et al. 2023

Sensors

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Partial discharge (PD) localization is important for monitoring and maintaining high-voltage equipment, which can help to prevent accidents. In this work, an acoustic localization sensor based on microelectromechanical system (MEMS) microphone array is proposed, which can detect and locate the partial discharge through a beam-forming algorithm. The MEMS microphone array consists of eight commercial MEMS microphones (SPV08A0LR5H-1, Knowles Electronics, IL, USA) with an aperture size of about 0.1 m × 0.1 m, allowing for a small hardware size and low cost. In order to optimize the acoustic performance of the array, a random array topology is designed. The simulation analysis indicates that the designed random topology is superior to several commonly used topologies. In terms of the localization algorithm, a deconvolution method called Fourier-based fast iterative shrinkage thresholding algorithm (FFT-FISTA) is applied. Simulation and experiment results demonstrate that FFT-FISTA used in the proposed acoustic localization sensor has significant advantages over the conventional beam-forming algorithm on spatial resolution and sidelobe suppression. Experimental results also show that the average localization error of the proposed scheme is about 0.04 m, which can meet the demands of practical application.

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Section: The Design Of Acoustic Localization Sensormentioning

confidence: 99%

An Acoustic Localization Sensor Based on MEMS Microphone Array for Partial Discharge

Yan

Chen

et al. 2023

Sensors

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“…To suppress the shift-variant of PSF, an irregular focus grid introduced in Xenaki et al 22 and Chu et al 25 as shown in Figure 2(b) is applied. For convenience comparison, the imaging region size of the irregular focus grid is taken the same as that of the regular focus grid.…”

Section: Simulationsmentioning

confidence: 99%

“…Like other Fourier-based deconvolution algorithms, FFT-FISTA with irregular focus point grid can overcome the deficiency of failing to accurately identify the sound source far from the center of the calculation plane. 25 FFT-FISTA is derived from FISTA and FISTA is originally proposed to solve the linear inverse problem in image processing. 26 Bhotto et al 27 introduced a positive definite weight matrix in the gradient function minimization of FISTA and proposed improved FISTA (IFISTA) to enhance the convergence and the image reconstruction accuracy of FISTA.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

“…27 introduced a positive definite weight matrix in the gradient function minimization of FISTA and proposed improved FISTA (IFISTA) to enhance the convergence and the image reconstruction accuracy of FISTA. Inspired by Lylloff et al., 24 Chu et al., 25 Beck and Teboulle, 26 and Bhotto et al., 27 to further improve the computational efficiency and the convergence of FFT-FISTA, a Fourier-based IFISTA (FFT-IFISTA) for acoustic source identification is proposed in this paper.…”

Section: Introductionmentioning

confidence: 99%

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A novel Fourier-based deconvolution algorithm with improved efficiency and convergence

Shen

Chu

Zhang

et al. 2019

Journal of Low Frequency Noise, Vibration and Active Control

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Various deconvolution algorithms for acoustic source are developed to improve spatial resolution and suppress sidelobe of the conventional beamforming. To improve the computational efficiency and solution convergence of deconvolution, this paper proposes a Fourier-based improved fast iterative shrinkage thresholding algorithm. Simulations and experiments show that Fourier-based improved fast iterative shrinkage thresholding algorithm can achieve excellent acoustic identification performance, with high computational efficiency and good convergence. For Fourier-based improved fast iterative shrinkage thresholding algorithm, the larger the weight coefficient, the narrower the mainlobe width, and the better the convergence, but the spurious source also increases. The recommended weight coefficient for the array described herein is 3. In addition, like other Fourier-based deconvolution algorithms, Fourier-based improved fast iterative shrinkage thresholding algorithm using irregular focus grid can obtain better acoustic source identification performance than using the conventional regular focus grid.

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Comparison of reconstruction algorithm for compressive sensing magnetic resonance imaging

Kong

2017

Multimed Tools Appl

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Improvement of Fourier-based fast iterative shrinkage-thresholding deconvolution algorithm for acoustic source identification

Cited by 15 publications

References 12 publications

An Acoustic Localization Sensor Based on MEMS Microphone Array for Partial Discharge

An Acoustic Localization Sensor Based on MEMS Microphone Array for Partial Discharge

A novel Fourier-based deconvolution algorithm with improved efficiency and convergence

Comparison of reconstruction algorithm for compressive sensing magnetic resonance imaging

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