- ISSN 0258-2724
- CN 51-1277/U
- EI Compendex
- Scopus
- Indexed by Core Journals of China, Chinese S&T Journal Citation Reports
- Chinese S&T Journal Citation Reports
- Chinese Science Citation Database
| Citation: | ZHANG Xin, ZHAI Linglu, WANG Jianshen, ZHANG Zhi, WU Chen. Weighted Fusion-Based Unscented Kalman Filter Positioning Algorithm for Normal-Conducting High-Speed Maglev Trains[J]. Journal of Southwest Jiaotong University, 2024, 59(4): 832-838. doi: 10.3969/j.issn.0258-2724.20230501
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The positioning and speed measurement method for normal-conducting high-speed maglev trains based on tooth slot detection of long stator may have inaccurate positioning caused by the lack of speed measurement and positioning signal, interference, and installation error of speed measurement and positioning during maglev train operation. Therefore, in order to improve the accuracy of positioning and speed measurement of high-speed maglev trains, an unscented Kalman filter (UKF) speed measurement and positioning algorithm for normal-conducting high-speed maglev trains based weighted fusion was proposed. The speed measurement and positioning method for high-speed maglev trains based on the tooth slot of a long stator was introduced, and the multi-channel redundant speed and position information was pre-treated, adaptively weighted, and fused. The UKF speed measurement and positioning algorithm for normal-conducting high-speed maglev trains based on weighted fusion was given. Based on the speed measurement and positioning in-loop test of the maglev train on a testbed, the improved UKF maglev position algorithm was compared with the original positioning algorithm. The analysis shows that the average speed error of the maglev train is reduced by 32.6%, and the speed range is reduced by 49.3%, which effectively eliminates the signal acquisition noise and improves the accuracy of speed measurement and positioning of maglev trains.
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