A covariance matrix reconstruction approach for single snapshot direction of arrival estimation

Muhammad, M., Li, M., Abbasi, Q. , Goh, C. and Imran, M. A. (2022) A covariance matrix reconstruction approach for single snapshot direction of arrival estimation. Sensors, 22(8), 3096. (doi: 10.3390/s22083096)

Abstract

Achieving accurate single snapshot direction of arrival (DOA) information significantly improves communication performance. This paper investigates an accurate and high-resolution DOA estimation technique by enabling single snapshot data collection and enhancing DOA estimation results compared to multiple snapshot methods. This is carried out by manipulating the incoming signal covariance matrix while suppressing undesired additive white Gaussian noise (AWGN) by actively updating and estimating the antenna array manifold vector. We demonstrated the estimation performance in simulation that our proposed technique supersedes the estimation performance of existing state-of-the-art techniques in various signal-to-noise ratio (SNR) scenarios and single snapshot sampling environments. Our proposed covariance-based single snapshot (CbSS) technique yields the lowest root-mean-squared error (RMSE) against the true DOA compared to root-MUSIC and the partial relaxation (PR) approach for multiple snapshots and a single signal source environment. In addition, our proposed technique presents the lowest DOA estimation performance degradation in a multiple uncorrelated and coherent signal source environment by up to 25.5% with nearly negligible bias. Lastly, our proposed CbSS technique presents the best DOA estimation results for a single snapshot and single-source scenario with an RMSE of 0.05° against the true DOA compared to root-MUSIC and the PR approach with nearly negligible bias as well. A potential application for CbSS would be in a scenario where accurate DOA estimation with a small antenna array form factor is a limitation, such as in the intelligent transportation system industry and wireless communication.

• Item Type: Articles
• Additional Information: This research and APC was supported in part by the Singapore Economic Development Board (EDB) and RFNet Technologies Private Limited, Singapore.
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Goh, Dr Cindy Sf and Imran, Professor Muhammad and Abbasi, Professor Qammer and Bin Muhammad, Murdifi
• Authors: Muhammad, M., Li, M., Abbasi, Q., Goh, C., and Imran, M. A.
• College/School: College of Science and Engineering > School of Engineering > Autonomous Systems and Connectivity; College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
• Journal Name: Sensors
• Publisher: MDPI
• ISSN: 1424-8220
• ISSN (Online): 1424-8220
• Published Online: 18 April 2022
• Copyright Holders: Copyright © 2022 The Authors
• First Published: First published in Sensors 22(8): 3096
• Publisher Policy: Reproduced under a Creative Commons License