Abstract
Since the positioning accuracy of sensors degrades due to noise and environmental interference when a single sensor is used to localize a suspended rare-earth permanent magnetically levitated train, a multi-sensor information fusion method using multiple sensors and self-correcting weighting is proposed for permanent magnetic levitated train localization. A decay memory factor is introduced to reduce the weight of the influence of historical measurement data on the fusion estimation, thus enhancing the robustness of the fusion algorithm. The Kalman filtering results suffer from inaccuracy when process noise is present in the system. In this paper, we use a covariance adaptive scheme that replaces the prediction step of the Kalman filter with covariance. It uses the covariance adaptive scheme to search the posterior sequence online and reconstruct the prior error covariance. Since the process noise covariance is not used in the new adaptive scheme, the negative impact of the mismatch noise statistics is greatly reduced. Simulation and experimental results show that the use of multi-sensor information fusion and covariance adaptive Kalman algorithm has significant advantages in terms of adaptability, accuracy and simplicity.
Funder
the Jiangxi Provincial Natural Science Foundation
the Central Guided Local Science and Technology Funding Project of the Science and Technology Department of Jiangxi Province
the 03 Special Project and 5G Program of the Science and Technology Department of Jiangxi Province
the Program of Qingjiang Excellent Young Talents in Jiangxi University of Science and Technology
Key Research and Development Program of Jiangxi Province
a grant from the Research Projects of Ganjiang Innovation Academy, Chinese Academy of Sciences
State Key Laboratory of Long-life High Temperature Materials
Publisher
Public Library of Science (PLoS)
Reference42 articles.
1. Simulation and experimental research on electromagnetic radiation from suspended permanent magnetic levitation train;YC Wang;International Journal of Applied Electromagnetics and Mechanics,2022
2. Track defect detection for high-speed maglev trains via deep learning;YX He;IEEE Transactions on Instrumentation and Measurement,2022
3. Research on the development trend of new railway technology and suggestions to China;LY Wang;China Railway,2020
4. Superconducting electromagnetic launch system for civil aircraft;L Bertola;IEEE Transactions on Applied Superconductivity,2016
5. Maglev Technology and Research Trends on Superconductivity;M Tomita;Quarterly Report of RTRI,2023
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