5G-Enabled Vehicle Positioning Using EKF With Dynamic Covariance Matrix Tuning-Reference-Cited by-同舟云学术

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5G-Enabled Vehicle Positioning Using EKF With Dynamic Covariance Matrix Tuning

Published:2022 Issue:3 Volume:45 Page:192-198
ISSN:2694-1783
Container-title:IEEE Canadian Journal of Electrical and Computer Engineering
language:
Short-container-title:IEEE Can. J. Electr. Comput. Eng.

Author:

Saleh Sharief¹^ORCID,El-Wakeel Amr S.²^ORCID,Noureldin Aboelmagd³^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, Queen’s University, Kingston, Canada

2. Lane Department of Computer Science and Electrical Engineering, West Virginia University, Morgantown, WV, USA

3. School of Computing, Queen’s University, Kingston, Canada

Funder

Natural Sciences and Engineering Research Council of Canada

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Medicine

Link

http://xplorestaging.ieee.org/ielx7/9349829/9944186/09881502.pdf?arnumber=9881502

Reference19 articles.

1. mmWave Simultaneous Localization and Mapping Using a Computationally Efficient EK-PHD Filter

2. A survey on 5G massive MIMO localization

3. Massive MIMO is a reality—What is next?

4. Reliable Positioning and mmWave Communication via Multi-Point Connectivity

5. Localization Performance Evaluation of Extended Kalman Filter in Wireless Sensors Network

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3. High-Order Extended Kalman Filter for State Estimation of Nonlinear Systems;Symmetry;2024-05-16

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