An adaptive diagonal loading technique to improve direction of arrival estimation accuracy for linear antenna array sensors

Muhammad, M., Li, M., Abbasi, Q. H. , Goh, C. and Imran, M. A. (2022) An adaptive diagonal loading technique to improve direction of arrival estimation accuracy for linear antenna array sensors. IEEE Sensors Journal, 22(11), pp. 10986-10994. (doi: 10.1109/JSEN.2022.3168785)

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Abstract

Diagonal loading is one of the most widely used and effective methods to improve the robustness of both adaptive beamformers and Direction of Arrival (DOA) estimation due to the involvement of the sensor received covariance matrix. In addition, subspace-based DOA estimation techniques rely on multiple snapshots to achieve high estimation accuracies. This paper presents the study of a modified diagonally loaded sample covariance matrix for accurate DOA estimation in adverse scenarios. The proposed and novel technique deciphers poor DOA estimation in a low SNR environment by computationally changing the received sample covariance matrix. Our method is computationally simple as it does not require peak searching and does not depend on the coherency of the signal. The efficacy of the proposed method is examined via computer simulation for various sensor array sizes and the number of snapshot samples. Based on our numerical simulation results, our proposed method generally outperforms most state-of-the-art DOA estimators. In a finite number of snapshots and a single signal source, our proposed method performs 9.5% better than the state-of-the-art DOA estimation technique, 2.8% in multiple signal sources, and 8.5% in a single snapshot, single signal source environment of gained DOA estimation performance.

Item Type:Articles
Additional Information:The authors would like to acknowledge and express sincere appreciation to the Singapore Economic Development Board (EDB) and RFNet Technologies Pte Ltd for financing and providing a good environment and facilities to support the project.
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. H., 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:IEEE Sensors Journal
Publisher:IEEE
ISSN:1530-437X
ISSN (Online):1558-1748
Published Online:18 April 2022
Copyright Holders:Copyright © 2022 IEEE
First Published:First published in IEEE Sensors Journal 22(11): 10986-10994
Publisher Policy:Reproduced in accordance with the publisher copyright policy

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