Fast and High-Resolution Acoustic Beamforming: A Convolution Accelerated Deconvolution Implementation

Chu, Ning; Zhao, Han; Yu, Liang; Huang, Qikai; Ning, Yue

doi:10.1109/tim.2020.3043869

Cited by 11 publications

(6 citation statements)

References 47 publications

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“…According to Equation (43), this calculation includes a matrix decomposition process and an iterative deconvolution process, and its computational complexity is

[ 40 ].…”

Section: Problem Formulation and Proposed Methodsmentioning

confidence: 99%

“…Considering the iterative process in deconvolution, some studies have tested the time of deconvolution and proposed some algorithms for accelerating deconvolution. In [ 40 ], the author approximates the power propagation matrix to a symmetric STBT matrix, and deduces the regularized deconvolution algorithm of the convolution kernel for convolution. They reduced the computational complexity from

.…”

Section: Introductionmentioning

confidence: 99%

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An Optimal Subspace Deconvolution Algorithm for Robust and High-Resolution Beamforming

Miao

Sun

et al. 2022

Sensors

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Utilizing the difference in phase and power spectrum between signals and noise, the estimation of direction of arrival (DOA) can be transferred to a spatial sample classification problem. The power ratio, namely signal-to-noise ratio (SNR), is highly required in most high-resolution beamforming methods so that high resolution and robustness are incompatible in a noisy background. Therefore, this paper proposes a Subspaces Deconvolution Vector (SDV) beamforming method to improve the robustness of a high-resolution DOA estimation. In a noisy environment, to handle the difficulty in separating signals from noise, we intend to initial beamforming value presets by incoherent eigenvalue in the frequency domain. The high resolution in the frequency domain guarantees the stability of the beamforming. By combining the robustness of conventional beamforming, the proposed method makes use of the subspace deconvolution vector to build a high-resolution beamforming process. The SDV method is aimed to obtain unitary frequency matrixes more stably and improve the accuracy of signal subspaces. The results of simulations and experiments show that when the input SNR is less than −27 dB, signals of decomposition differ unremarkably in the subspace while the SDV method can still obtain clear angles. In a marine background, this method works well in separating the noise and recruiting the characteristics of the signal into the DOA for subsequent processing.

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“…According to Equation (43), this calculation includes a matrix decomposition process and an iterative deconvolution process, and its computational complexity is

[ 40 ].…”

Section: Problem Formulation and Proposed Methodsmentioning

confidence: 99%

.…”

Section: Introductionmentioning

confidence: 99%

An Optimal Subspace Deconvolution Algorithm for Robust and High-Resolution Beamforming

Miao

Sun

et al. 2022

Sensors

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show abstract

“…• 3D Computed Tomography [18,22,43] • Acoustical imaging [44][45][46][47][48][49] • Hyperspectral imaging [50] • Spectrometry [51] • Eddy current tomography [52] • Non destructive testing applications [53] • Emission Tomography [54]…”

Section: References To Examples Of Applicationsmentioning

confidence: 99%

Bayesian Inference for Inverse Problems

Mohammad‐Djafari¹

2022

Bayesian Inference - Recent Advantages

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Inverse problems arise everywhere we have indirect measurement. Regularization and Bayesian inference methods are two main approaches to handle inverse problems. Bayesian inference approach is more general and has much more tools for developing efficient methods for difficult problems. In this chapter, first, an overview of the Bayesian parameter estimation is presented, then we see the extension for inverse problems. The main difficulty is the great dimension of unknown quantity and the appropriate choice of the prior law. The second main difficulty is the computational aspects. Different approximate Bayesian computations and in particular the variational Bayesian approximation (VBA) methods are explained in details.

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“…Acoustic source localization in acoustical imaging can also be considered as an inverse problem, where the positions and intensities of acoustical sources have to be estimated from the signal received by the microphone arrays. If we represent the distribution of the sources by

each microphone receives the sum of the delayed sources’ sounds [ 42 , 43 , 44 , 45 ]:

where

is the delay of transmission from the source position n to the microphone position m . This delay is a function of the speed of the sound and the distance between the source position

and the microphone position

.…”

Section: Inverse Problems Examplementioning

confidence: 99%

Regularization, Bayesian Inference, and Machine Learning Methods for Inverse Problems

Mohammad-Djafari

2021

Entropy

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Classical methods for inverse problems are mainly based on regularization theory, in particular those, that are based on optimization of a criterion with two parts: a data-model matching and a regularization term. Different choices for these two terms and a great number of optimization algorithms have been proposed. When these two terms are distance or divergence measures, they can have a Bayesian Maximum A Posteriori (MAP) interpretation where these two terms correspond to the likelihood and prior-probability models, respectively. The Bayesian approach gives more flexibility in choosing these terms and, in particular, the prior term via hierarchical models and hidden variables. However, the Bayesian computations can become very heavy computationally. The machine learning (ML) methods such as classification, clustering, segmentation, and regression, based on neural networks (NN) and particularly convolutional NN, deep NN, physics-informed neural networks, etc. can become helpful to obtain approximate practical solutions to inverse problems. In this tutorial article, particular examples of image denoising, image restoration, and computed-tomography (CT) image reconstruction will illustrate this cooperation between ML and inversion.

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Fast and High-Resolution Acoustic Beamforming: A Convolution Accelerated Deconvolution Implementation

Cited by 11 publications

References 47 publications

An Optimal Subspace Deconvolution Algorithm for Robust and High-Resolution Beamforming

An Optimal Subspace Deconvolution Algorithm for Robust and High-Resolution Beamforming

Bayesian Inference for Inverse Problems

Regularization, Bayesian Inference, and Machine Learning Methods for Inverse Problems

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