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

Ruru Mei; Ye Tian; Yonghui Huang; Zhugang Wang

doi:10.3390/s22103754

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

Sensors (Basel). 2022 May 14;22(10):3754. doi: 10.3390/s22103754.

Authors

Ruru Mei^{1

2}, Ye Tian^{1

2}, Yonghui Huang¹, Zhugang Wang¹

Affiliations

¹ National Space Science Center, Chinese Academy of Sciences, Beijing 100190, China.
² University of Chinese Academy of Sciences, Beijing 100049, China.

Abstract

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.

Keywords: 2D-DOA estimation; covariance matrix completion; deep learning; neural network; uniform circular array.

MeSH terms

Algorithms
Animals
Brachyura*
Deep Learning*
Neural Networks, Computer

Grants and funding

This research was funded by the Strategic Priority Research Program of the Chinese Academy of Sciences (Class A) (Subject Number: XDA153501, Sub-Subject Number: XDA15350103).