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UNet-rootMUSIC: A high accuracy direction of arrival estimation method under array imperfection

Nguyen, D.-T. and Le, T.-H. and Doan, V.-S. and Hoang, V.-P. (2024) UNet-rootMUSIC: A high accuracy direction of arrival estimation method under array imperfection. AEU - International Journal of Electronics and Communications, 173.

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Abstract

In the practical direction-finding systems, the accuracy and resolution of the direction of arrival (DOA) estimation are affected not only by the Gaussian noise and array size but also by hardware configuration imperfections, such as errors in element manufacturing and mounting. These impairments cause phase and amplitude errors in estimating the DOA of signal sources. To address this issue, this paper proposes to combine a U-shape deep neural network (UNet) with the multiple signal classification via the root of the polynomial (rootMUSIC) algorithm (so-called UNet-rootMUSIC) to improve the DOA estimation accuracy. In this approach, the UNet model plays a role in converting a covariance matrix of received signals containing phase and gain errors into a nearly perfect one of the ideal antenna array. The rootMUSIC algorithm is then employed to estimate the signal DOA based on the converted covariance matrix. The DOA estimation performance of the uniform linear array of eight elements with an inter-element distance of λ/2 is analyzed through experimental simulations. The simulation results demonstrate that our method can significantly reduce the root mean square error of DOA estimation compared to the conventional MUSIC, rootMUSIC, ESPRIT methods and two deep neural network-based angular classification methods. © 2023 Elsevier GmbH

Item Type: Article
Divisions: Offices > Office of International Cooperation
Identification Number: 10.1016/j.aeue.2023.155008
Uncontrolled Keywords: Antenna arrays; Convolutional neural networks; Covariance matrix; Deep neural networks; Errors; Gaussian noise (electronic); Mean square error; Multiple signal classification; Radio communication, Automatic modulation; Automatic modulation classification; Convolutional neural network; Covariance matrices; Direction of arrival estimation; Directionof-arrival (DOA); High-accuracy; Intelligent radio communication; Modulation classification; Phase error, Direction of arrival
Additional Information: cited By 0
URI: http://eprints.lqdtu.edu.vn/id/eprint/11010

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