Augmented subspace MUSIC method for DOA estimation using acoustic vector sensor array

Liu, Aifei; Yang, Deseng; Shi, Shengguo; Zhu, Zhaojie; Liu, Ying

doi:10.1049/iet-rsn.2018.5440

Cited by 42 publications

(9 citation statements)

References 28 publications

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“…is indicates again that the proposed method is robust against the nonuniform noise. [1,4,3,8,5,7,10](i.e., WNRP � 10). We carry out 500 independent trials and show the results in Figure 10.…”

Section: Comparisons With Prior Artsmentioning

confidence: 99%

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Reweighted Covariance Fitting Based on Nonconvex Schatten-p Minimization for Gridless Direction of Arrival Estimation

Alabideen

Al-Dakkak

Khorzom

2020

Mathematical Problems in Engineering

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In this paper, we reformulate the gridless direction of arrival (DoA) estimation problem in a novel reweighted covariance fitting (CF) method. The proposed method promotes joint sparsity among different snapshots by means of nonconvex Schatten-p quasi-norm penalty. Furthermore, for more tractable and scalable optimization problem, we apply the unified surrogate for Schatten-p quasi-norm with two-factor matrix norms. Then, a locally convergent iterative reweighted minimization method is derived and solved efficiently via a semidefinite program using the optimization toolbox. Finally, numerical simulations are carried out in the background of unknown nonuniform noise and under the consideration of coprime array (CPA) structure. The results illustrate the superiority of the proposed method in terms of resolution, robustness against nonuniform noise, and correlations of sources, in addition to its applicability in a limited number of snapshots.

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Section: Comparisons With Prior Artsmentioning

confidence: 99%

“…DoA estimation is a major task in array signal processing. It has wide applications in radar, sonar, wireless communications, underwater acoustics, and seismology [1][2][3].…”

Section: Introductionmentioning

confidence: 99%

Reweighted Covariance Fitting Based on Nonconvex Schatten-p Minimization for Gridless Direction of Arrival Estimation

Alabideen

Al-Dakkak

Khorzom

2020

Mathematical Problems in Engineering

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“…The classical known techniques/algorithms are beamforming, maximum-likelihood, the subspace method, time-difference of arrival (TDoA), etc. [28][29][30][31][32]. However, due to the presence of noise, interference and reverberations cause a higher deviation from the true value of the DoA, therefore being less suitable for many applications.…”

Section: Introductionmentioning

confidence: 99%

Improved Direction-of-Arrival Estimation of an Acoustic Source Using Support Vector Regression and Signal Correlation

Alam

Usman

Alkhammash

et al. 2021

Sensors

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The direction-of-arrival (DoA) estimation of an acoustic source can be estimated with a uniform linear array using classical techniques such as generalized cross-correlation, beamforming, subspace techniques, etc. However, these methods require a search in the angular space and also have a higher angular error at the end-fire. In this paper, we propose the use of regression techniques to improve the results of DoA estimation at all angles including the end-fire. The proposed methodology employs curve-fitting on the received multi-channel microphone signals, which when applied in tandem with support vector regression (SVR) provides a better estimation of DoA as compared to the conventional techniques and other polynomial regression techniques. A multilevel regression technique is also proposed, which further improves the estimation accuracy at the end-fire. This multilevel regression technique employs the use of linear regression over the results obtained from SVR. The techniques employed here yielded an overall 63% improvement over the classical generalized cross-correlation technique.

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“…The presence of distortion causes inaccurate estimation of DOAs of an acoustic source. Techniques for DOA estimation with small errors in the presence of impairments use different acoustic vector sensors and microphone/sensor array configurations (Bogaert et al , 2011; Wajid et al , 2017a, b, 2019, 2020a, b; Yadav et al , 2020; Alam et al , 2021; Liu et al , 2019). The DOA estimation techniques are classified as based on regression, beamforming and subspace method.…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of direction-finding techniques of an acoustic source with uniform linear array

Uddin

Khan

Wajid

et al. 2021

FEBE

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PurposeThe purpose of this paper is to show a comparative study of different direction-of-arrival (DOA) estimation techniques, namely, multiple signal classification (MUSIC) algorithm, delay-and-sum (DAS) beamforming, support vector regression (SVR), multivariate linear regression (MLR) and multivariate curvilinear regression (MCR).Design/methodology/approachThe relative delay between the microphone signals is the key attribute for the implementation of any of these techniques. The machine-learning models SVR, MLR and MCR have been trained using correlation coefficient as the feature set. However, MUSIC uses noise subspace of the covariance-matrix of the signals recorded with the microphone, whereas DAS uses the constructive and destructive interference of the microphone signals.FindingsVariations in root mean square angular error (RMSAE) values are plotted using different DOA estimation techniques at different signal-to-noise-ratio (SNR) values as 10, 14, 18, 22 and 26dB. The RMSAE curve for DAS seems to be smooth as compared to PR1, PR2 and RR but it shows a relatively higher RMSAE at higher SNR. As compared to (DAS, PR1, PR2 and RR), SVR has the lowest RMSAE such that the graph is more suppressed towards the bottom.Originality/valueDAS has a smooth curve but has higher RMSAE at higher SNR values. All the techniques show a higher RMSAE at the end-fire, i.e. angles near 90°, but comparatively, MUSIC has the lowest RMSAE near the end-fire, supporting the claim that MUSIC outperforms all other algorithms considered.

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Augmented subspace MUSIC method for DOA estimation using acoustic vector sensor array

Cited by 42 publications

References 28 publications

Reweighted Covariance Fitting Based on Nonconvex Schatten-p Minimization for Gridless Direction of Arrival Estimation

Reweighted Covariance Fitting Based on Nonconvex Schatten-p Minimization for Gridless Direction of Arrival Estimation

Improved Direction-of-Arrival Estimation of an Acoustic Source Using Support Vector Regression and Signal Correlation

Performance evaluation of direction-finding techniques of an acoustic source with uniform linear array

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