融合轨枕检测和卡尔曼滤波的中低速磁浮列车测速定位优化算法研究
潘洪亮1,2邱宇1唐少强1
Speed and Position Detection Optimization Algorithm of Medium and Low Speed Maglev Train Based on Integrating of Rail Sleeper Detection and Kalman Filter
PAN HongliangQiu YuTang Shaoqiang
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作者信息:1.同济大学道路与交通工程教育部重点实验室， 201804， 上海
2.同济大学磁浮交通工程技术研究中心，201804， 上海
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Affiliation:Key Laboratory of Road and Traffic Engineering of the Ministry of Education, Tongji University, 201804, Shanghai, China
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关键词:
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Key words:
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DOI:10.16037/j.1007-869x.2022.03.004
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中图分类号/CLCN:U237;U284.48+1
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栏目/Col:研究报告
摘要:
可靠、准确的速度位置信息对于中低速磁浮列车的安全行驶起着至关重要的作用。基于中低速磁浮列车测速定位中常用的轨枕计数检测方法，采用2套涡流传感器冗余设备采集列车的速度、位移数据，提出不同速度下列车测速定位的优化算法。在该优化算法的基础上融合了卡尔曼滤波，通过不断迭代和更新，得到准确的速度位置信息，并计算出采用2套测速设备时2组速度数据的计算权值。采用M atlab软件对优化算法进行仿真，结果表明:该算法可以显著提高中低速磁浮列车速度位置信息采集的精度和可靠性。
Abstracts:
Reliable and accurate speed and position information plays an important role in the safe operation of medium and low speed maglev train. Based on the commonly used rail sleeper count detection method in the speed measurement and positioning of medium and low speed maglev train, two sets of eddy current sensor redundant equipment are adopted to collect train speed, displacement data. The speed and position detection optimization algorithm of the train at different speeds is put forward. Kalman filter is integrated on the basis of the optimization algorithm. Through continuous iteration and update, accurate speed and position information is obtained, and the calculation weights of the two sets of speed data by adopting two sets of speed measuring equipment are calculated. Matlab software is used to simulate the optimization algorithm. The results show that the algorithm can significantly improve the accuracy and reliability of medium and low speed maglev train speed and position information acquisition.
引文 / Ref:
潘洪亮，邱宇，唐少强.融合轨枕检测和卡尔曼滤波的中低速磁浮列车测速定位优化算法研究[J].城市轨道交通研究，2022，25（3）:15.
PAN Hongliang, QIU Yu, TANG Shaoqiang. Speed and position detection optimization algorithm of medium and low speed maglev train based on integrating of rail sleeper detection and Kalman filter [J].Urban mass transit, 2022，25（3）:11.
PAN Hongliang, QIU Yu, TANG Shaoqiang. Speed and position detection optimization algorithm of medium and low speed maglev train based on integrating of rail sleeper detection and Kalman filter [J].Urban mass transit, 2022，25（3）:11.
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