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Article

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

by 1,2, 1,2, 1 and 1,*
1
National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China
2
University of Chinese Academy of Sciences, Beijing 100049, China
*
Author to whom correspondence should be addressed.
Academic Editors: Ying-Ren Chien, Mu Zhou, Ao Peng, Ni Zhu and Joaquín Torres-Sospedra
Sensors 2022, 22(10), 3754; https://doi.org/10.3390/s22103754
Received: 19 April 2022 / Revised: 11 May 2022 / Accepted: 12 May 2022 / Published: 14 May 2022
(This article belongs to the Special Issue Signal Processing and Machine Learning for Smart Sensing Applications)
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. View Full-Text
Keywords: 2D-DOA estimation; uniform circular array; covariance matrix completion; neural network; deep learning 2D-DOA estimation; uniform circular array; covariance matrix completion; neural network; deep learning
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MDPI and ACS Style

Mei, R.; Tian, Y.; Huang, Y.; Wang, Z. 2D-DOA Estimation in Switching UCA Using Deep Learning-Based Covariance Matrix Completion. Sensors 2022, 22, 3754. https://doi.org/10.3390/s22103754

AMA Style

Mei R, Tian Y, Huang Y, Wang Z. 2D-DOA Estimation in Switching UCA Using Deep Learning-Based Covariance Matrix Completion. Sensors. 2022; 22(10):3754. https://doi.org/10.3390/s22103754

Chicago/Turabian Style

Mei, Ruru, Ye Tian, Yonghui Huang, and Zhugang Wang. 2022. "2D-DOA Estimation in Switching UCA Using Deep Learning-Based Covariance Matrix Completion" Sensors 22, no. 10: 3754. https://doi.org/10.3390/s22103754

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