智能手机GNSS/MEMS IMU车载组合导航的安装角估计算法

王甫红, 程雨欣, 赵广越, 张万威

王甫红, 程雨欣, 赵广越, 张万威. 智能手机GNSS/MEMS IMU车载组合导航的安装角估计算法[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20230381
引用本文: 王甫红, 程雨欣, 赵广越, 张万威. 智能手机GNSS/MEMS IMU车载组合导航的安装角估计算法[J]. 武汉大学学报 ( 信息科学版). DOI: 10.13203/j.whugis20230381
WANG Fuhong, CHENG Yuxin, ZHAO Guangyue, ZHANG Wanwei. Estimate the Mounting Angles of the IMU for the Smartphone-Based Vehicular GNSS/MEMS IMU Integrated System[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20230381
Citation: WANG Fuhong, CHENG Yuxin, ZHAO Guangyue, ZHANG Wanwei. Estimate the Mounting Angles of the IMU for the Smartphone-Based Vehicular GNSS/MEMS IMU Integrated System[J]. Geomatics and Information Science of Wuhan University. DOI: 10.13203/j.whugis20230381

智能手机GNSS/MEMS IMU车载组合导航的安装角估计算法

基金项目: 

国家自然科学基金(62103307,62073044);卫星导航系统与装备技术国家重点实验室开放基金(CEPNT-2021KF-05)。

详细信息
    作者简介:

    王甫红,博士,教授,主要从事卫星导航定位和卫星定轨方法的教学与研究工作。fhwang@sgg.whu.edu.cn

Estimate the Mounting Angles of the IMU for the Smartphone-Based Vehicular GNSS/MEMS IMU Integrated System

  • 摘要: 针对智能手机车载导航时因安装角未知而降低全球卫星导航系统(global navigation satellite system,GNSS)/惯性导航系统(inertial navigation system,INS)组合导航精度与性能的问题,在智能手机GNSS/微机械惯性测量单元(micro-electro-mechanical system inertial measurement unit,MEMS IMU)紧组合导航算法的基础上,提出以载波相位时间差分(time-differenced carrier phase,TDCP)计算得出的历元间位置变化量来构建安装角估计的观测方程,并在线估计智能手机车载导航安装角的卡尔曼滤波算法。在典型城市环境的两组车载试验结果表明:本算法在较为严重的遮挡环境下可在80 s内实现大安装角初始化,安装角滤波器收敛后安装角波动在2°以内。安装角估计成功后,使用非完整性约束(non-holonomic constraint,NHC)算法,严重遮挡条件下车载组合导航的精度与性能得到明显提升,平面位置与高程的精度统计均能维持在5 m左右。本算法不需要增加里程计等辅助设备,能够较快实现智能手机大安装角的精确估计。
    Abstract: Objectives: Recognizing the fact that the unknown inertial measurement unit (IMU) mounting angles, i.e., the misalignment angles between the smartphone's IMU and the vehicle body frame, can seriously affect the smartphone-based Global Navigation Satellite System (GNSS)/inertial navigation system (INS) integrated system for vehicular navigation. Methods: In this contribution, a real-time estimation of the IMU mounting angles is addressed through a Kalman Filter (KF) whose measurement updates are realized by position variation between epochs derived by time-differenced carrier phase (TDCP) besides the smartphone GNSS/MEMS IMU tightly-coupled integration algorithm. Results: Two vehicular navigation experiments were carried out in urban challenging environments to evaluate the performance of the proposed algorithm. The results show that a large mounting angle can be initialized within 80 seconds under moderate occlusion conditions, then convergence of the estimated mounting angle is shown to occur within minutes, and the accuracy is in the order of 2°. When the mounting angle is estimated successfully, the positioning performance of smartphone vehicular positioning will be improved significantly, both horizontal and vertical accuracy can be maintained at about 5m in terms of root-mean-square by applying non-holonomic constraint (NHC) under severe occlusion conditions. Conclusions: The algorithm proposed can estimate large mounting angles accurately and rapidly without any auxiliary equipment such as an odometer.
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出版历程
  • 收稿日期:  2024-01-27
  • 网络出版日期:  2024-03-13

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