基于GNSS/DSRC融合的协同车辆定位方法

刘江; 蔡伯根; 王云鹏

基于GNSS/DSRC融合的协同车辆定位方法

刘江^{1, 2,},
蔡伯根¹,
王云鹏^{3, 4}

1.
北京交通大学电子信息工程学院, 北京 100044
2.
北京交通大学轨道交通控制与安全国家重点实验室, 北京 100044
3.
北京航空航天大学交通科学与工程学院, 北京 100191
4.
北京航空航天大学车路协同与安全控制北京市重点实验室, 北京 100191

基金项目:

国家自然科学基金项目 U1334211

国家自然科学基金项目 61273089

中央高校基本科研业务费专项资金项目 2014JBM003

国家国际科技合作专项项目 2014DFA80260

北京市自然科学基金项目 4144081

详细信息

作者简介:
刘江(1985-), 男, 陕西汉中人, 北京交通大学讲师, 工学博士, 从事智能交通系统研究

中图分类号: U495
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- 被引次数: 0
出版历程
- 收稿日期: 2014-03-01
- 刊出日期: 2014-08-25

Cooperative vehicle positioning method based on GNSS/DSRC fusion

LIU Jiang^{1, 2
,},
CAI Bo-gen¹,
WANG Yun-peng^{3, 4}

1.
School of Electronic and Information Engineering, Beijing Jiaotong University, Beijing 100044, China
2.
State Key Laboratory of Rail Traffic Control and Safety, Beijing Jiaotong University, Beijing 100044, China
3.
School of Transportation Science and Engineering, Beihang University, Beijing 100191, China
4.
Beijing Key Laboratory for Cooperative Vehicle Infrastructure Systems and Safety Control, Beihang University, Beijing 100191, China

More Information

Author Bio:
LIU Jiang(1985-), male, lecturer, PhD, +86-10-51684361, jiangliu@bjtu.edu.cn

摘要

摘要: 以全球导航卫星系统(GNSS)定位与专用短程无线通信(DSRC)协同定位的集成信息融合为目标, 在DSRC协同定位层面, 基于水平精度因子最小化原则, 提出了一种邻车节点的优选策略。在GNSS/DSRC融合定位层面, 采用分散式融合估计思想, 设计了一种松耦合模式下的车辆组合定位方法, 基于GNSS、DSRC并行滤波进行全局估计, 利用反馈策略改善了对不同定位条件的适应能力。利用车路协同仿真平台对协同车辆定位方法进行了仿真验证。验证结果表明: 邻车节点优选策略显著提升了DSRC定位精度, 将其用于GNSS/DSRC融合定位, 在常规运行条件下, 带反馈机制的分散式估计精度优于单传感器模式与无反馈分散式估计精度; 在给定的GNSS多径干扰条件下, 东向、北向位置估计的均方根误差与单GNSS模式相比分别降低了42.6%和37.0%, 与集中式融合估计相比分别降低了24.8%和20.3%。协同车辆定位方法的定位性能优于常规定位方案, 对GNSS多径干扰条件具有良好的适应能力, 具备更优的精确性、可用性及工程应用价值。
- 全球导航卫星系统 /
- 专用短程无线通信 /
- 协同车辆定位 /
- 信息融合 /
- 滤波估计
Abstract: Aiming at the integration and information fusion of global navigation satellite system(GNSS)positioning and the cooperative positioning based on dedicated short-range communication(DSRC), an advanced selection strategy for neighborhood vehicle nodes for DSRC-based cooperative positioning was proposed based on the minimum principle of horizontal dilution of precision.For GNSS/DSRC integrated vehicle positioning, a loose-coupling positioning method was designed according to decentralized estimation scheme.In the positioning method, the parallel GNSS and DSRC filters were combined for global estimation, and the adaptive capability against different operation conditions was enhanced based on feedback strategy.The cooperative vehicle positioning method was verified by using a cooperative vehicle infrastructure simulation platform.Verification result indicates that the precision of DSRC-based positioning method was significantly improved by the selection strategy of neighborhood vehicle nodes.When the DSRCbased positioning method was used in the GNSS/DSRC integrated positioning, under normal operation condition, the precision of decentralized fusion estimation with feedback is better than that with single sensor mode and the decentralized estimation without feedback.Compared to the GNSS-alone mode under the given GNSS multipath interference condition, the root mean square errors of vehicle positioning method reduce by 42.6% and 37.0% in east and north direction respectively, and they reduce by 24.8% and 20.3% compared to the centralized fusion estimation.The performance of proposed positioning method is better than conventional positioning solutions, and has an enhanced tolerant ability to GNSS multipath interference conditions, which suggest the better precision, availability and practical application value of proposed method.
- global navigation satellite system /
- dedicated short-range communication /
- cooperative vehicle positioning /
- information fusion /
- filter estimation

HTML全文

图 1 D_E分布

Figure 1. Distribution of D_E

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图 2 D_N分布

Figure 2. Distribution of D_N

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图 3 仿真路网

Figure 3. Simulated road network

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图 4 目标车辆运行轨迹

Figure 4. Running trajectory of target vehicle

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图 5 可用邻车数量分布

Figure 5. Distribution of available neighborhood vehicle number

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图 6 DSRC东向定位误差

Figure 6. DSRC positioning errors in east direction

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图 7 DSRC北向定位误差

Figure 7. DSRC positioning errors in north direction

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图 8 HDOP取值空间与策略2、3HDOP差值

Figure 8. Space of HDOP values and differences of HDOP values between strategy 2 and 3

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图 9 目标车辆运行轨迹估计

Figure 9. Estimated running trajectories of target vehicle

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图 10 六种策略东向定位误差比较

Figure 10. Comparison of positioning errors in east direction under six strategies

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图 11 六种策略下北向定位误差比较

Figure 11. Comparison of positioning errors in north direction under six strategies

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图 12 DSRC、GNSS滤波器信息分配系数

Figure 12. Information distribution coefficients of DSRC and GNSS filters

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图 13 不同GNSS信号条件下的HDOP值

Figure 13. HDOP values under different GNSS signal conditions

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图 14 不同GNSS信号条件下的可用卫星数

Figure 14. Available satellite numbers under different GNSS signal conditions

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图 15 不同条件下东向定位误差比较

Figure 15. Comparison of positioning errors in east direction under different conditions

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图 16 不同条件下北向定位误差比较

Figure 16. Comparison of positioning errors in north direction under different conditions

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表 1 不同策略的均方根误差比较

Table 1. Comparison of RMSE values under different strategies

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