Name
Title | ||
|---|---|---|
2D-DOA Estimation in Switching UCA Using Deep Learning-Based Covariance Matrix Completion |
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. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.3390/s22103754 | SENSORS |
Keywords | DocType | Volume |
2D-DOA estimation, uniform circular array, covariance matrix completion, neural network, deep learning | Journal | 22 |
Issue | ISSN | Citations |
10 | 1424-8220 | 0 |
PageRank | References | Authors |
0.34 | 0 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ruru Mei | 1 | 0 | 0.68 |
| Ye Tian | 2 | 0 | 1.35 |
| Yonghui Huang | 3 | 2 | 2.81 |
| Zhugang Wang | 4 | 1 | 0.69 |