[1]汪涛,童志华,张萼辉.列车多源测速测距融合方法及测速偏差补偿的实现[J].高速铁路技术,2025,16(06):111-118.[doi:10.12098/j.issn.1674-8247.2025.06.016]
　WANG Tao,TONG Zhihua,ZHANG Ehui.Multi-source Velocity and Distance Measurement Fusion Method for Trains and Implementation of Measurement Deviation Compensation[J].HIGH SPEED RAILWAY TECHNOLOGY,2025,16(06):111-118.[doi:10.12098/j.issn.1674-8247.2025.06.016]
点击复制

列车多源测速测距融合方法及测速偏差补偿的实现()

《高速铁路技术》[ISSN:1674-8247/CN:51-1730/U]

卷:
16卷
期数:
2025年06期
页码:
111-118
栏目:
装备系统
出版日期:
2025-12-30

文章信息/Info

Title:
Multi-source Velocity and Distance Measurement Fusion Method for Trains and Implementation of Measurement Deviation Compensation
文章编号:
1674-8247(2025)06-0111-08
作者:
汪涛; 童志华; 张萼辉
(中国铁路上海局集团有限公司, 上海 200080)
Author(s):
WANG Tao;  TONG Zhihua;  ZHANG Ehui
(China Railway Shanghai Group Co., Ltd., Shanghai 200080, China)
关键词:
列车测速测距;  卡尔曼滤波;  多源融合;  轮径误差补偿;  异步观测更新
Keywords:
train velocity and distance measurement;  Kalman Filter;  multi-source fusion;  wheel diameter error compensation;  asynchronous observation update
分类号:
U284.48
DOI:
10.12098/j.issn.1674-8247.2025.06.016
文献标志码:
A
摘要:
为提高列车测速测距系统的精度与可靠性,本文提出一种基于多源融合的测速测距方法。该方法以扩展卡尔曼滤波器(Extended Kalman Filter,EKF)为核心,通过李群上的低通滤波器实现 IMU 加速度向轨道坐标系的映射,以抑制重力泄漏误差; 设计观测触发的异步更新结构,适应传感器异步采样与观测缺失; 并在状态空间中引入轮径尺度因子与加速度零偏,实现速度尺度误差的在线估计与补偿。基于含轨道不平顺、打滑/空转、电磁干扰及 GNSS 遮挡的仿真平台进行验证,结果表明该方法在抗轮径偏差与漂移抑制方面优于传统方案,在平均速度270 km/h无距离校正条件下测距漂移为 9 ppm,具备良好的工程应用潜力。
Abstract:
To enhance the accuracy and reliability of train velocity and distance measurement systems, this paper proposed a multi-source fusion-based velocity and distance measurement method. This method employed an Extended Kalman Filter(EKF)as its core. A low-pass filter on the Lie group was utilized to map IMU accelerations onto the track coordinate system, thereby suppressing gravity leakage error. An observation-triggered asynchronous update architecture was designed to accommodate asynchronous sensor sampling and missing observations. Furthermore, a wheel diameter scale factor and acceleration bias were incorporated into the state space to enable online estimation and compensation of velocity scale errors. This method was validated on a simulation platform incorporating track irregularities, wheel slip/slide, electromagnetic interference, and GNSS signal occlusion. The results show that the proposed method outperforms traditional schemes in resisting wheel diameter deviation and suppressing drift. Under conditions of an average speed of 270 km/h and without distance correction, the distance measurement drift is 9 ppm, indicating significant potential for engineering applications.

参考文献/References:

[1] 曾凡祥, 李勤英. 卡尔曼滤波在动态测量系统中的应用[J]. 北京测绘, 2014, 28(3): 47-51.
ZENG Fanxiang, LI Qinying. Application of Kalman Filter in Dynamic Surveying System[J]. Beijing Surveying and Mapping, 2014, 28(3): 47-51.
[2] LARSSON M. Sensor Fusion Application to Railway Odometry[J]. School of Computer Science & Communication, 2014.
[3] REIMER C, MÜLLER F J, HINÜBER E L V. INS/GNSS/Odometer Data Fusion in Railway Applications[C]//2016 DGON Intertial Sensors and Systems(ISS). September 20-21, 2016, Karlsruhe, Germany. IEEE, 2016: 1-14.
[4] LI Xiaoping, ZHANG Jianbin. Innovation Adaptive UKF Train Location Method Based on Kinematic Constraints[J]. Electronics, 2024, 13(19): 3958.
[5] 李兆洋, 刘红娇, 李毅, 等. 基于图像处理的高速铁路轨道巡检系统[J]. 高速铁路技术, 2023, 14(3): 21-26.
LI Zhaoyang, LIU Hongjiao, LI Yi, et al. A High-speed Railway Track Inspection System Based on Image Processing[J]. High Speed Railway Technology, 2023, 14(3): 21-26.
[6] SIMON IWNICKI. Handbook of Railway Vehicle Dynamics, 2nd ed.[M]. CRC Press, 2019.
[7] 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): 15-20.
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 Kalm an Filter[J]. Urban Mass Transit, 2022, 25(3): 15-20.
[8] 杨翼, 刘江, 胡永兴. 基于北斗/INS迭代卡尔曼滤波的列车完整性检测方法研究[J]. 铁道通信信号, 2016, 52(4): 60-63.
YANG Yi, LIU Jiang, HU Yongxing. Research of Train Integrity Detection Method Based on BDS/INS Iteration Kalman Filter[J]. Railway Signalling & Communication, 2016, 52(4): 60-63.
[9] IWNICKI S, IWNICKI S. Handbook of Railway Vehicle Dynamics[M]. Boca Raton: CRC Press, 2006.
[10] 翟婉明. 车辆-轨道耦合动力学-上册, [M]. 4版. 北京:科学出版社, 2015.
ZHAI Wanming. Vehicle-track Coupled Dynamics Volume 1[M]. 4th ed. Beijing: Science Press, 2015.
[11] 吴萌岭, 彭顺, 李小平. 列车轮轨黏着力在线估测计算方法[J]. 同济大学学报(自然科学版), 2018, 46(3): 354-358, 388.
WU Mengling, PENG Shun, LI Xiaoping. Online Estimation Algorithm of Adhesive Force for Train Wheeltrack[J]. Journal of Tongji University(Natural Science), 2018, 46(3): 354-358, 388.
[12] SIMON D. Optimal State Estimation: Kalman, H∞, and Nonlinear Approaches[J]. 2006.
[13] ZHAI W. Vehicle-track Coupled Dynamics: Theory and Applications[M]. 2020.
[14] SENGUPTA, MONISH. Location and Speed Estimation for Telematic Signalling in Railways [D] University College London, 2024.
[15] LI QING, ROBERT WEBER. GNSS/IMU/Odometer Based Train Positioning [DB/OL].(2020-05-04)[2025-07-08] https://meetingorganizer.copernicus.org/EGU2020/EGU2020-7856.html

备注/Memo

备注/Memo:
收稿日期:2025-07-08
作者简介:汪涛(1997-),男,助理工程师。
更新日期/Last Update: 2025-12-30