Fast Direction of Arrival Estimation for Uniform Circular Arrays With a Virtual Signal Subspace

Xu, Kaijie; Quan, Yinghui; Bie, Bowen; Xing, Mengdao; Nie, Weike; Hanyu, E

doi:10.1109/taes.2021.3050667

Cited by 17 publications

(5 citation statements)

References 56 publications

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“…Compared with the uniform linear array, which can only estimate one-dimensional DOA, the uniform circular array can realize two-dimensional DOA estimation such as azimuth and elevation angles, and the resolution at different azimuth angles is isotropic. Compared with the uniform linear array, which can only estimate the one-dimensional DOA [6] , the uniform circular array can realize the two-dimensional DOA estimation such as azimuth and elevation angle [7] , and the resolution at different azimuth angles is isotropic [8] . The sound source localization algorithm directly processes the speech data received by the microphone array, and processes the data to estimate the position of the sound source more accurately.…”

Section: Introductionmentioning

confidence: 99%

DOA estimation based on mode and maximum eigenvector algorithm with reverberation environment

Liu,

et al. 2024

Third International Conference on Algorithms, Microchips, and Network Applications (AMNA 2024)

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Section: Introductionmentioning

confidence: 99%

DOA estimation based on mode and maximum eigenvector algorithm with reverberation environment

Liu,

et al. 2024

Third International Conference on Algorithms, Microchips, and Network Applications (AMNA 2024)

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“…These layers allow DL models to learn complex data and make accurate predictions. In this paper, specifically, the MUSIC algorithm is chosen as the DOA estimation method because of the following advantages [19,20]: the ability to simultaneously measure multiple signals, capacity for high precision measurement, high resolution for antenna beam signals, and insensitivity to array geometry.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Enabled Improved Direction-of-Arrival Estimation Technique

Jenkinson,

Abbasi,

Molaei

et al. 2023

Electronics

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This paper provides a simple yet effective approach to improve direction-of-arrival (DOA) estimation performance in extreme signal-to-noise-ratio (SNR) conditions. As an example, a multiple signal classification (MUSIC) algorithm with a deep learning (DL) approach is used. First, brief research into the existing DOA estimation techniques is provided, followed by a demonstration of a simulation environment created on the MATLAB platform to generate and resolve signals from a uniform rectangular array of antenna elements. Following that is an attempt to improve the estimation accuracy of these signals by training various DL approaches, including multi-layer perceptron and one- and two-dimensional convolutional neural networks, using the generated dataset. Key findings include the cases where the developed DL approach can resolve signals and provide accurate DOA estimations that the MUSIC algorithm cannot.

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“…Two-dimensional direction of arrival (2D-DOA) estimation is a critical issue in the domain of array signal processing and has a wide range of applications such as wireless communication [ 1 ], sonar [ 2 ], radar [ 3 ], etc. Meanwhile, the uniform circular array (UCA) is a widely utilized configuration in the context of 2D-DOA estimation due to its attractive abilities, including covering 360° azimuthal space and providing elevation angle information [ 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

2D-DOA Estimation in Switching UCA Using Deep Learning-Based Covariance Matrix Completion

Mei

Tian

Huang

et al. 2022

Sensors

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In this paper, we study the two-dimensional direction of arrival (2D-DOA) estimation problem in a switching uniform circular array (SUCA), which means performing 2D-DOA estimation with a reduction in the number of radio frequency (RF) chains. We propose a covariance matrix completion algorithm for 2D-DOA estimation in a SUCA. The proposed algorithm estimates the complete covariance matrix of a fully sampled UCA (FUCA) from the sample covariance matrix of the SUCA through a neural network. Afterwards, the MUSIC algorithm is performed for 2D-DOA estimation with the completed covariance matrix. We conduct Monte Carlo simulations to evaluate the performance of the proposed algorithm in various scenarios; the performance of 2D-DOA estimation in the SUCA gradually approaches that in the FUCA as the SNR or the number of snapshots increases, which means that the advantages of a FUCA can be preserved with fewer RF chains. In addition, the proposed algorithm is able to implement underdetermined 2D-DOA estimation.

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Fast Direction of Arrival Estimation for Uniform Circular Arrays With a Virtual Signal Subspace

Cited by 17 publications

References 56 publications

DOA estimation based on mode and maximum eigenvector algorithm with reverberation environment

DOA estimation based on mode and maximum eigenvector algorithm with reverberation environment

Deep Learning-Enabled Improved Direction-of-Arrival Estimation Technique

2D-DOA Estimation in Switching UCA Using Deep Learning-Based Covariance Matrix Completion

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