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基于加权融合的常导高速磁浮列车UKF定位算法

张昕 翟凌露 王舰深 张志 吴晨

张昕, 翟凌露, 王舰深, 张志, 吴晨. 基于加权融合的常导高速磁浮列车UKF定位算法[J]. 西南交通大学学报. doi: 10.3969/j.issn.0258-2724.20230501
引用本文: 张昕, 翟凌露, 王舰深, 张志, 吴晨. 基于加权融合的常导高速磁浮列车UKF定位算法[J]. 西南交通大学学报. doi: 10.3969/j.issn.0258-2724.20230501
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. doi: 10.3969/j.issn.0258-2724.20230501
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. doi: 10.3969/j.issn.0258-2724.20230501

基于加权融合的常导高速磁浮列车UKF定位算法

doi: 10.3969/j.issn.0258-2724.20230501
基金项目: 山东省重点研发计划(2020CXGC010202)
详细信息
    作者简介:

    张昕(1976—),女,副教授,博士,研究方向为车辆工程,E-mail:xinzhang@bjtu.edu.cn

  • 中图分类号: U237

Weighted Fusion-Based Unscented Kalman Filter Positioning Algorithm for Normal-Conducting High-Speed Maglev Trains

  • 摘要:

    为了提高高速磁浮列车定位测速的精确性,本文针对基于长定子齿槽检测的常导高速磁浮列车测速定位方法,在列车运行过程中可能因测速定位信号缺失、干扰、测速定位安装误差等原因引起的定位不准问题,提出一种基于加权融合无迹卡尔曼滤波(UKF)的常导高速磁浮列车测速定位算法. 介绍了高速磁浮列车基于长定子齿槽的测速定位方法,并对多路冗余速度位置信息进行预处理和自适应加权融合处理;给出基于加权融合UKF的常导高速磁浮列车测速定位算法模型;基于磁浮列车测速定位在环测试试验台试验,对改进后的无迹卡尔曼滤波磁浮定位算法与原定位算法进行了对比分析. 分析结果表明:磁浮列车平均速度误差减小了32.6%,速度极差降低了49.3%,有效消除了信号采集噪声,提高了磁浮列车测速定位精度.

     

  • 图 1  基于长定子齿槽检测的测速定位示意

    Figure 1.  Speed measurement and positioning based on tooth slot detection of long stator

    图 2  多路定位信号融合算法流程

    Figure 2.  Flow chart of multi-channel positioning signal fusion algorithm

    图 3  车载环境模拟与在环测试设备

    Figure 3.  Vehicle environment simulation and in-loop test equipment

    图 4  速度测量值和实际速度对比

    Figure 4.  Comparison of measured speed and actual speed

    图 5  速度测量值与基于加权融合的UKF改进速度对比

    Figure 5.  Comparison of measured speed and speed after application of UKF based on weighted fusion

    图 6  改进前后速度误差对比

    Figure 6.  Comparison of speed errors before and after improvement

    图 7  头车右路速度测量值与应用基于加权融合的UKF算法后的速度对比

    Figure 7.  Comparison of measured right-channel speeds of head train and speed after application of UKF algorithm based on weighted fusion

    图 8  采用基于加权融合的UKF改进前、后的头车左、右两路速度差值

    Figure 8.  Difference between right/left-channel speeds of head train before and after application of UKF based on weighted fusion

  • [1] 邹逸鹏. 高速磁浮列车系统动力学仿真与参数优化研究[D]. 成都:西南交通大学,2021.
    [2] 蔡煊,王长林. 基于改进UKF的BDS/IMU组合列车定位方法[J]. 西南交通大学学报,2020,55(2): 393-400.

    CAI Xuan, WANG Changlin. BeiDou navigation satellite system/inertial measurement unit integrated train positioning method based on improved unscented Kalman filter algorithm[J]. Journal of Southwest Jiaotong University, 2020, 55(2): 393-400.
    [3] SAAB S S. A map matching approach for train positioning I. development and analysis[J]. IEEE Transactions on Vehicular Technology, 2000, 49(2): 467-475. doi: 10.1109/25.832978
    [4] 亓宝进. 磁浮列车测速定位系统硬件在环仿真系统研究[D]. 北京:北京交通大学,2022.
    [5] 孟川舒. 高速磁浮列车测速定位问题综述[J]. 铁道标准设计,2024,68(1): 178-184,211.

    MENG Chuanshu. Review on problems of speed measurement and positioning of high speed maglev train[J]. Railway Standard Design, 2024, 68(1): 178-184,211.
    [6] 张世聪. 适用于磁浮列车的测速定位方法研究综述[J]. 铁道标准设计,2018,62(10): 186-191.

    ZHANG Shicong. Research review of speed and position detection methods applied to maglev trains[J]. Railway Standard Design, 2018, 62(10): 186-191.
    [7] 罗桂斌. 高速磁浮定位测速系统信号处理技术研究[D]. 长沙:国防科技大学,2017.
    [8] 窦峰山,何洪礼,谢云德,等. 基于跟踪微分器的磁浮列车定位测速系统信号处理问题研究[J]. 铁道学报,2016,38(1): 81-85.

    DOU Fengshan, HE Hongli, XIE Yunde, et al. Research on the signal processing of position and speed detection system in maglev train based on tracking differentiator[J]. Journal of the China Railway Society, 2016, 38(1): 81-85.
    [9] 吴峻,周文武,李璐. 高速磁浮列车测速定位系统的研究[J]. 国防科技大学学报,2011,33(1): 109-114.

    WU Jun, ZHOU Wenwu, LI Lu. Research on speed and position detection system of high speed maglev train[J]. Journal of National University of Defense Technology, 2011, 33(1): 109-114.
    [10] 唐志一,蔡颖,王会. 基于自适应加权算法的多传感器数据融合方法[J]. 指挥信息系统与技术,2022,13(5): 66-70.

    TANG Zhiyi, CAI Ying, WANG Hui. Multi? sensor data fusion method based on adaptive weighting algorithm[J]. Command Information System and Technology, 2022, 13(5): 66-70.
    [11] 张亮. 基于EKF的GPS/ODO列车定位方法研究[D]. 北京:北京交通大学,2016.
    [12] JIANG Z Q, LIU C H, ZHANG G, et al. GPS/INS integrated navigation based on UKF and simulated annealing optimized SVM[C]//2013 IEEE 78th Vehicular Technology Conference (VTC Fall). Las Vegas: IEEE, 2013: 1-5.
    [13] 赵玏洋,闫利. 移动机器人SLAM位姿估计的改进四元数无迹卡尔曼滤波[J]. 测绘学报,2022,51(2): 212-223.

    ZHAO Leyang, YAN Li. Advanced quaternion unscented Kalman filter based on SLAM of mobile robot pose estimation[J]. Acta Geodaetica et Cartographica Sinica, 2022, 51(2): 212-223.
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出版历程
  • 收稿日期:  2023-09-26
  • 修回日期:  2024-04-28
  • 网络出版日期:  2024-06-11

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