DOA estimation of rail transit tunnel far field signals based on reconstructive subspace MUSIC

Yanliang Jin; Rukun Lyu; Yuan Gao; Guoxing Zheng

doi:10.1117/12.2667279

22 February 2023 DOA estimation of rail transit tunnel far field signals based on reconstructive subspace MUSIC

Yanliang Jin, Rukun Lyu, Yuan Gao, Guoxing Zheng

Author Affiliations +

Proceedings Volume 12587, Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022); 125870H (2023) https://doi.org/10.1117/12.2667279
Event: Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022), 2022, Shanghai, China

Abstract

For the rail traffic, feasibility of 5th Generation (5G) mobile communication massive Multiple Input Multiple Output (MIMO) used in the tunnel is studying. In order to effectively set up base station, antenna and intelligent reflecting surface (IRS), the direction of arrival (DOA) must be captured. Aiming at the problem of poor performance of two-dimensional MUltiple SIgnal Classification (2D-MUSIC) algorithm in the environment of low signal-to-noise ratio (SNR), small snapshots and small incident angle interval signals, an improved MUSIC algorithm with uniform rectangular array (URA) based on reconstructive subspace is proposed. By reconstructing subspace, a new spatial spectrum is obtained in terms of subspace eigenvectors. Then, the DOAs are obtained by searching the maximum of the new spatial spectrum. High estimation accuracy and angular resolution are often required in practical applications of rail traffic 5G system, and there exist tunnel scenarios that we need the improved MUSIC algorithm has the ability to conduct two-dimensional DOA estimation. Simulations and the measured data of straight tunnel scenario are used to verify the effectiveness of the proposed algorithm and its higher searching precision in complex signal environments such as low SNR, strong-to-weak proximity, and coherent interference.

Citation Download Citation

Yanliang Jin, Rukun Lyu, Yuan Gao, and Guoxing Zheng "DOA estimation of rail transit tunnel far field signals based on reconstructive subspace MUSIC", Proc. SPIE 12587, Third International Seminar on Artificial Intelligence, Networking, and Information Technology (AINIT 2022), 125870H (22 February 2023); https://doi.org/10.1117/12.2667279

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available

Members: $17.00

Non-members: $21.00 ADD TO CART

PROCEEDINGS
8 PAGES

DOWNLOAD PAPER SAVE TO MY LIBRARY

GET CITATION

RIGHTS & PERMISSIONS

Get copyright permission Get copyright permission on Copyright Marketplace

KEYWORDS

Multiple input multiple output

Reconstruction algorithms

Signal to noise ratio

Eigenvectors

Expectation maximization algorithms

Environmental sensing

Transmitters

Show All Keywords

Keywords/Phrases

Search In:

Publication Years