基于改进自适应IMM算法的高速列车组合定位

王小敏; 雷筱; 张亚东

doi:10.11999/JEIT230251

基于改进自适应IMM算法的高速列车组合定位

doi: 10.11999/JEIT230251

王小敏^{1, 2},
雷筱^1, ,,
张亚东^{1, 2}

1.
西南交通大学信息科学与技术学院成都 611756
2.
四川省列车运行控制技术工程研究中心成都 611756

基金项目: 中国国家铁路集团有限公司科技研究开发计划(P2021G053, N2021T008, N2021G045, N2022G010)；上海航天科技创新基金(SAST2020-126)

详细信息

作者简介:
王小敏：男，教授，研究方向为轨道交通运行控制

雷筱：男，硕士生，研究方向为列车定位

张亚东：男，副教授，研究方向为列车运行控制理论与技术

通讯作者:
雷筱　m18781012607@163.com

中图分类号: TN967.2; TP391
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- 文章访问数: 522
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- PDF下载量: 72
- 被引次数: 0
出版历程
- 收稿日期: 2023-04-11
- 修回日期: 2023-08-21
- 录用日期: 2023-08-21
- 网络出版日期: 2023-08-24
- 刊出日期: 2024-03-27

Combined Positioning of High-Speed Train Based on Improved Adaptive IMM Algorithm

WANG Xiaomin^{1, 2},
LEI Xiao^{1
, ,},
ZHANG Yadong^{1, 2}

1.
School of Information Science and Technology, Southwest Jiaotong University, Chengdu 611756, China
2.
Sichuan Province Train Operation Control Technology Engineering Research Center, Chengdu 611756, China

Funds: The Science and Technology Research and Development Program of China National Railway Group Corporation (P2021G053, N2021T008, N2021G045, N2022G010), Shanghai Aerospace Science and Technology Innovation Fund funded projects (SAST2020-126)

摘要

摘要: 针对列车高精度定位问题，该文提出基于改进自适应交互多模型(IMM)的高速列车高精度组合定位方法。首先，根据列车定位需求和各传感器特点，设计了卫星接收器、轮轴测速传感器、测速雷达以及单轴陀螺仪4种传感器的组合定位方案。然后，针对IMM融合滤波算法因先验信息不准导致固定参数设置不当的问题，引入Sage-Husa自适应滤波和转移概率矩阵(TPM)自适应更新集成为自适应IMM算法。针对多模型切换的滞后问题，利用子模型似然函数值能快速反映模型变化趋势的特点，将似然函数值设为判定标志，并引入判定窗对TPM矩阵元素进行修正，有效提升了模型的切换速度。最后，基于改进自适应IMM算法对4种传感器定位信息进行融合滤波，实现高速列车的高精度组合定位。仿真结果表明：改进后的算法相比其他自适应IMM算法提升定位精度1.6%～14.7%，并且能通过提高模型间切换速度来有效降低位置误差峰值，同时具备较好的抗噪性能。
- 列车定位 /
- 交互式多模型 /
- Sage-Husa自适应滤波算法 /
- 马尔可夫转移概率矩阵 /
- 判定窗
Abstract: A high accuracy combined positioning method for high-speed trains based on the Improved Adaptive Interacting Multiple Model (IMM) is proposed for the high-precision positioning problem of trains. Firstly, a combined positioning scheme of four sensors, namely, satellite receiver, wheel speed sensor, speed radar and single-axis gyroscope, is designed according to the train positioning requirements and the characteristics of each sensor. Next, to address the issue that the IMM fusion filtering algorithm has improper fixed parameter settings due to inaccurate a priori information, the Sage-Husa adaptive filtering and the Transition Probability Matrix (TPM) adaptive update set are introduced to become the adaptive IMM algorithm. To solve the lag problem of multi-model switching, the likelihood function value is set as the judgment flag by using the feature that sub-model likelihood function value can quickly respond to the model change trend, and the judgment window is introduced to correct the TPM matrix elements, which effectively improves the model switching speed. Finally, based on the improved adaptive IMM algorithm, the fusion filtering of four sensor positioning information is carried out to realize the high-precision combined positioning of high-speed trains. Simulation results show that the enhanced algorithm improves the positioning accuracy by 1.6%～14.7% compared with other adaptive IMM algorithms, and it can effectively reduce the peak positional error by increasing the switching speed between models, and it also has a better anti-noise performance.
- Train positioning /
- Improved Adaptive Interacting Multiple Model (IMM) /
- Sage-Husa adaptive filter /
- Markov Transition Probability Matrix (TPM) /
- Judgement window

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图 1 高速列车组合定位方案

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图 2 本文改进的自适应IMM算法框架

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图 3 算法位置误差比较

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图 4 CV切换至CA时算法模型概率变化曲线

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图 5 CV切换至CT时算法模型概率变化曲线

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图 6 传感器量测噪声方差自适应调整

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图 7 场景2下算法位置误差比较

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表 1 列车定位传感器特性

传感器	类别	提供的信息	精度下降的场景	优势	误差来源
轮轴测速传感器	相对定位	速度	车轮粘着不良	低成本、高可靠	空转打滑、轮径磨损
车载多普勒雷达	相对定位	速度	极端积水	高精度	安装误差、车体振动
卫星接收器	直接定位	位置/速度	封闭空间	低成本、高精度	受卫星接收条件影响
INS	相对定位	加速度/角速度	无	高精度、高可靠	累计误差

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表 2 CRH3型车的动力学参数

参数	取值
轮径(mm)	920
牵引质量(t)	536
回转系数	0.08
基本阻力(N)	0.79+0.0064v+0.000115v²
牵引(kN)	–0.285v+300, v≤119 km/h, 31500/v, v＞119 km/h
制动加速度(m/s²)	–0.00043v+0.7105, v＞210 km/h , –0.0021v+1.0612, 172 km/h＜v≤210 km/h , –0.025v+5, 160 km/h＜v≤172 km/h, 1 km/h＜v≤160 km/h, 0

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表 3 各滤波算法的定位性能比较

算法	东方向位移MAE(m)	东方向位移最大偏差(m)	北方向位移MAE(m)	北方向位移最大偏差(m)	位置MAE(m)	位置RMSE(m)	最大位置偏差(m)
文献[5]	0.5090	2.1361	0.4788	2.0156	0.7690	0.8707	2.8537
标准IMM	0.4304	2.2686	0.4278	2.4337	0.6719	0.7801	2.9912
文献[13]	0.4244	2.4353	0.4147	2.9316	0.6551	0.7628	3.1984
文献[16]	0.4729	2.1850	0.4743	2.3048	0.7411	0.8456	2.6531
文献[14]	0.3892	2.3567	0.3876	2.8491	0.6082	0.7360	3.6359
文献[15]	0.3865	2.7326	0.3849	2.9204	0.6040	0.7325	3.5502
本文	0.3874	2.1912	0.3765	2.3092	0.5964	0.7209	2.7817

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表 4 传感器实际精度

传感器	雷达(m/s)	轮轴测速传感器(m/s)	卫星(m)
噪声标准差(V≥100 km/h)	$\dfrac{{{\text{0}}{\text{.4}}{f_0}\cos \theta }}{{3{\text{c}} \cdot 3.6}}$	$\dfrac{{0.2\% NV\Delta t}}{{3\pi D \cdot 3.6}}$	1
噪声标准差(V＜100 km/h)	$\dfrac{{{\text{0}}{\text{.2\% }}V{f_0}\cos \theta }}{{3{\text{c}} \cdot 3.6}}$	$\dfrac{{0.6N\Delta t}}{{3\pi D \cdot 3.6}}$	1

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表 5 算法在场景2的定位性能对比

算法	东方向位移MAE(m)	北方向位移MAE(m)	位置RMSE(m)	最大位置偏差(m)
文献[5]	0.6678	0.6959	1.3752	6.2420
文献[15]	0.6055	0.6009	1.3268	7.6920
文献[17]	0.5615	0.5629	1.1176	5.9152
本文	0.4931	0.4947	0.9515	5.3753

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