Deconvolution algorithms of phased microphone arrays for the mapping of acoustic sources in an airframe test

Wang, Jiayu; Ma, Wei

doi:10.1016/j.apacoust.2020.107283

Cited by 10 publications

(3 citation statements)

References 32 publications

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“…where p − Aq 2 2 is known as measurement error, it could be interpreted the correctness of fitness for the liner system; q 2 2 is the regularizer with 2 norm. λ is a positive regularization parameter to balance the relative importance between the measurement error and sparsity.…”

Section: Sound Source Reconstruction Modelmentioning

confidence: 99%

“…The acoustic imaging techniques have played a central role in the Noise, Vibration, and Harshness (NVH) engineering field of sound source identification, such as automotive, aircraft, and industrial applications [ 1 , 2 , 3 ]. As one of the excellent acoustic imaging tools, near-field acoustical holography (NAH) was first formalized four decades ago by the groundbreaking work of Williams [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

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A Ratio Model of L1/L2 Norm for Sound Source Identification

Huang

Zhang

et al. 2020

Sensors

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In the field of sound source identification, robust and accurate identification of the targeted source could be a challenging task. Most of the existing methods select the regularization parameters whose value could directly affect the accuracy of sound source identification during the solving processing. In this paper, we introduced the ratio model ℓ1/ℓ2 norm to identify the sound source(s) in the engineering field. Using the alternating direction method of multipliers solver, the proposed approach could avoid the selection of the regularization parameter and localize sound source(s) with robustness at low and medium frequencies. Compared with other three methods employing classical penalty functions, including the Tikhonov regularization method, the iterative zoom-out-thresholding algorithm and the fast iterative shrinkage-thresholding algorithm, the Monte Carlo Analysis shows that the proposed approach with ℓ1/ℓ2 model leads to stable sound pressure reconstruction results at low and medium frequencies. The proposed method demonstrates beneficial distance-adaptability and signal-to-noise ratio (SNR)-adaptability for sound source identification inverse problems.

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Section: Sound Source Reconstruction Modelmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Ratio Model of L1/L2 Norm for Sound Source Identification

Huang

Zhang

et al. 2020

Sensors

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“…Specific source localisation generally involves beamforming. Recently, combined with different beamforming strategies, a large quantity of well-established acoustic beamforming algorithms have been reported, including functional beamforming [14], orthogonal beamforming [15], generalised inverse beamforming [16], and deconvolution beamforming [17]. Among them, deconvolution beamforming has increased interest in sound source localisation since it has the advantages of a smaller sidelobe than the conventional beamforming method.…”

Section: Introductionmentioning

confidence: 99%

An adaptive beamforming algorithm for sound source localisation via hybrid compressive sensing reconstruction

Guo¹,

Han²,

Chen³

et al. 2022

J. vibroeng.

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In this study, a hybrid compressive sensing reconstruction algorithm called SAMP-CoSaMP is proposed. The unique combination maintains the speed advantage of CoSaMP and the adaptive sparsity searching ability from the SAMP. Afterwards, an improved beamforming algorithm named SC-DAMAS for sound source localisation is created by integrating our hybrid algorithm with the classic DAMAS. Lastly, the reconstruction accuracy is compared between the SAMP-CoSaMP, SAMP, and CoSaMP algorithms in different signal-to-noise ratio scenarios. The results show that the SAMP-CoSaMP is balanced between running efficiency and reconstruction error. In addition, we perform comparative sound source localisation simulations and experiments by our SC-DAMAS with those of the conventional beamforming method and orthogonal matching pursuit algorithm-based deconvolution approach. SC-DAMAS is superior to the aforementioned counterparts in localisation performance without the need to predetermine the sparsity value.

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