基于SINS/RFID的隧道列车高精度定位方法

张辰东; 王兆瑞; 金声震; 艾国祥

doi:10.13700/j.bh.1001-5965.2020.0647

基于SINS/RFID的隧道列车高精度定位方法

doi: 10.13700/j.bh.1001-5965.2020.0647

张辰东^{1, 2},
王兆瑞^1, ,,
金声震¹,
艾国祥¹

1.
中国科学院国家天文台, 北京 100101
2.
中国科学院大学天文与空间科学学院, 北京 100049

基金项目:

国家重点研发计划 2016YFB0501900

国家自然科学基金 U1931125

国家自然科学基金 11603041

详细信息

通讯作者:
王兆瑞, E-mail: zhaorui_w@nao.cas.cn

中图分类号: TN967.2
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出版历程
- 收稿日期: 2020-11-20
- 录用日期: 2021-02-26
- 网络出版日期: 2022-04-20

High-precision positioning method based on SINS/RFID for trains in tunnel

1.
National Astronomical Observatories, Chinese Academy of Sciences, Beijing 100101, China
2.
School of Astronomy and Space Science, University of Chinese Academy of Sciences, Beijing 100049, China

Funds:

National Key R & D Program of China 2016YFB0501900

National Natural Science Foundation of China U1931125

National Natural Science Foundation of China 11603041

More Information

Corresponding author: WANG Zhaorui, E-mail: zhaorui_w@nao.cas.cn

摘要

摘要:
为了获取高速铁路列车在隧道这种导航卫星不可见环境下的定位信息，提出一种基于捷联惯性导航系统（SINS）和射频识别技术（RFID）的组合定位方法。通过响应时间模型来计算标签的定位精度，依据实际轨道环境增加标签对列车姿态校准的能力，同时结合惯性导航系统解算得到连续的定位数据。仿真结果表明：在30 km长的隧道利用射频识别标签位置信息进行校准，可以很大程度地减小惯性导航系统的误差积累，提高定位精度。引入姿态信息后，可以在陀螺仪性能与标签间隔的多种组合中保持隧道全线定位精度在米级，最高能够达到0.5 m。
- 组合导航 /
- 隧道定位 /
- 射频识别技术(RFID) /
- 捷联惯性导航系统(SINS) /
- 动态姿态校准
Abstract:
based on strapdown inertial navigation system (SINS) and radio frequency identification (RFID), an integrated positioning method is proposed for the position acquisition of high-speed trains in satellite-denied environment such as tunnels. The positioning accuracy of RFID tags is calculated by the response time model. The tags could also calibrate the attitude by adding actual railway information. Setting RFID tags on tunnel wall and at the same time combining with inertial navigation system provide continuous dynamic positioning data. Simulation results show that utilizing RFID tags in position calibration significantly decreases the error accumulation of inertial navigation system and increases positioning accuracy in 30 km tunnel. After the addition of attitude information, the positioning accuracy of the whole tunnel railway maintains at the level of meters in a variety of combinations of gyroscope performance and RFID tag interval, the best of which is 0.5 m.
- integrated navigation /
- tunnel positioning /
- radio frequency identification (RFID) /
- strapdown inertial navigation system (SINS) /
- dynamic attitude calibration

HTML全文

图 1 RFID标签精度分析

Figure 1. Precision analysis of RFID tag

下载: 全尺寸图片幻灯片

图 2 SINS/RFID组合导航系统结构

Figure 2. Structure of SINS/RFID integrated navigation system

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图 3 列车隧道轨迹曲线仿真

Figure 3. Curve of train trajectory simulation in tunnel

下载: 全尺寸图片幻灯片

图 4 RFID位置校准SINS的定位误差

Figure 4. Positioning error of SINS calibrated by position of RFID

下载: 全尺寸图片幻灯片

图 5 RFID姿态位置信息校准SINS的定位误差

Figure 5. Positioning error of SINS calibrated by position and attitude of RFID

下载: 全尺寸图片幻灯片

表 1 RFID阅读器读取位置分布统计

Table 1. Distribution statistics of received signal position of RFID reader

响应时间分布	均值/m	标准差/m
正态分布	1.921 9	0.337 3
均匀分布	1.908 7	0.358 9

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表 2 列车仿真轨迹的行进方案

Table 2. Scheme of train trajectory simulation

序号	路线方案	时间/s	重复次数
1	直行	50	1
2	左转，直行	5，5	7
3	左转，直行	45，5	1
4	右转，直行	5，5	1
5	左转，直行	5，5	2
6	右转，直行	5，5	5
7	左转，直行	5，5	1
8	右转，直行	5，5	4

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表 3 SINS/RFID位置组合导航定位误差统计

Table 3. Navigation positioning error statistics of SINS/RFID integrated by positon

间隔/m	情况1		情况2		情况3
间隔/m	平均值/m	最大值/m	平均值/m	最大值/m	平均值/m	最大值/m
100	0.752 8	1.153 3	3.104 4	5.682 5	27.824 4	51.267 9
200	1.374 4	1.993 8	6.150 7	11.315 7	56.235 7	107.712 2
500	3.187 6	4.505 5	15.423 3	27.781 1	141.115 7	254.971 2
1 000	6.202 6	9.020 6	30.243 2	52.392 2	276.417 9	538.789 2
不用RFID	93.333 2	122.828 6	338.633 0	653.079 8	3 072.103 3	6 425.889 1

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表 4 SINS/RFID位置姿态角组合导航定位误差统计

Table 4. Navigation positioning error statistics of SINS/RFID integrated by positon and attitude

间隔/m	情况1		情况2		情况3
间隔/m	平均值/m	最大值/m	平均值/m	最大值/m	平均值/m	最大值/m
100	0.510 4	0.743 4	0.573 8	0.851 9	0.625 2	1.002 6
200	0.845 7	1.413 4	0.968 0	1.427 1	1.183 1	1.987 6
500	1.812 3	2.936 7	2.158 1	3.580 0	3.789 5	6.411 3
1 000	3.497 4	6.041 7	4.507 6	9.521 8	11.982 4	24.171 1

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