Abstract
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.
Highlights
The determination of the direction-of-arrival (DOA) of an acoustic signal is a problem that is studied under the ambit of localization and tracking
Multiple signal classification (MUSIC), root MUSIC, estimation of signal parameters via rotational invariance technique (ESPRIT), etc. algorithms are present under the subcategory of the subspace method (Shi, 2019; Cui et al, 2019; Gupta et al, 2020; Varma, 2002; Zhou et al, 2017; Tang et al, 2014)
A comparative analysis of multiple techniques of DOA estimation, namely, support vector regression (SVR), RR, Polynomial-regression of order 1 (PR1), Polynomial-regression of order 2 (PR2), DAS and MUSIC, have been performed in this work. It has been revealed from the experiment that the MUSIC algorithm outperforms all other techniques in terms of root mean square angular error (RMSAE)
Summary
The determination of the direction-of-arrival (DOA) of an acoustic signal is a problem that is studied under the ambit of localization and tracking. New approaches based on deep neural networks (DNNs) for many speech sources localization using an array of smaller dimensions. These techniques result into high-resolution DOA estimation. The evaluation criteria include root meansquared error, accuracy and mean absolute error They evaluate this new deep and machine learning technology in DOA estimation, and various factors (signal-to-noise ratio, number of snapshots, number of antennas and number of signal sources) affecting DOA estimation are processed. It is being believed that advanced technologies like deep learning has improved the direction-finding techniques to a greater extent Such kind of study helps researchers to conduct detailed analysis (Ge et al, 2021; Zhang et al, 2021).
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