Motion model-assisted GNSS/MEMS-IMU integrated navigation system for land vehicle

被引:0
|
作者
Yaowen Sun
Zengke Li
Zhehua Yang
Kefan Shao
Wangqi Chen
机构
[1] China University of Mining and Technology,School of Environment Science and Spatial Informatics
来源
GPS Solutions | 2022年 / 26卷
关键词
GNSS; MEMS-IMU; Motion model; Robust; GNSS/MEMS-IMU integration;
D O I
暂无
中图分类号
学科分类号
摘要
Micro-electromechanical systems and inertial measurement units (MEMS-IMUs) show great advantages in terms of price and size. Nevertheless, due to limitations of technology, their observations are easily affected by the surrounding environment (temperature, vibration, and electronic noise). Most methods resist the effect of gross errors by adjusting covariance matrices in the integrated navigation of a global navigation satellite system (GNSS) and inertial navigation system (INS). We propose a motion model-assisted integrated navigation method on the basis of a constant yaw rate and velocity (CTRV) model, which serves as a constraint condition and detects gross errors by a Chi-squared test. The results of the CTRV are used to correct the carrier state from INS mechanization. A field test was carried out to verify the performance of the CTRV-assisted method. Compared with a robust Kalman filter, the method improves the horizontal accuracy of position and velocity by more than 87% and 68%, respectively, in a medium-precision loosely and tightly coupled system, and of the velocity and attitude by more than 52% and 20%, respectively, in a low-precision loosely and tightly coupled system. Therefore, the CTRV-assisted method can significantly enhance the performance of GNSS/MEMS-IMU integrated navigation systems.
引用
收藏
相关论文
共 50 条
  • [1] Motion model-assisted GNSS/MEMS-IMU integrated navigation system for land vehicle
    Sun, Yaowen
    Li, Zengke
    Yang, Zhehua
    Shao, Kefan
    Chen, Wangqi
    [J]. GPS SOLUTIONS, 2022, 26 (04)
  • [2] Improving MEMS-IMU/GPS integrated systems for land vehicle navigation applications
    Sasani, S.
    Asgari, J.
    Amiri-Simkooei, A. R.
    [J]. GPS SOLUTIONS, 2016, 20 (01) : 89 - 100
  • [3] Improving MEMS-IMU/GPS integrated systems for land vehicle navigation applications
    S. Sasani
    J. Asgari
    A. R. Amiri-Simkooei
    [J]. GPS Solutions, 2016, 20 : 89 - 100
  • [4] An integrated GPS/MEMS-IMU navigation system for an autonomous helicopter
    Wendel, Jan
    Meister, Oliver
    Schlaile, Christian
    Trommer, Gert F.
    [J]. AEROSPACE SCIENCE AND TECHNOLOGY, 2006, 10 (06) : 527 - 533
  • [5] Multiple model Kalman filtering for MEMS-IMU/GPS integrated navigation
    Tang Kang-Hua
    Wu Mei-Ping
    Hu Xiao-Ping
    [J]. ICIEA 2007: 2ND IEEE CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS, VOLS 1-4, PROCEEDINGS, 2007, : 2062 - 2066
  • [6] Performance Investigation of Real-time MEMS-IMU/GNSS Integrated System
    Zhang, Jieying
    Knedlik, Stefan
    Loffeld, Otmar
    [J]. PROCEEDINGS OF THE 22ND INTERNATIONAL TECHNICAL MEETING OF THE SATELLITE DIVISION OF THE INSTITUTE OF NAVIGATION (ION GNSS 2009), 2009, : 978 - 986
  • [7] Lie group based nonlinear state errors for MEMS-IMU/GNSS/magnetometer integrated navigation
    Cui, Jiarui
    Wang, Maosong
    Wu, Wenqi
    He, Xiaofeng
    [J]. JOURNAL OF NAVIGATION, 2021, 74 (04): : 887 - 900
  • [8] Heading accuracy improvement of MEMS IMU/DGPS integrated navigation system for land vehicle
    Gu, Dongqing
    El-Sheimy, Naser
    [J]. 2008 IEEE/ION POSITION, LOCATION AND NAVIGATION SYMPOSIUM, VOLS 1-3, 2008, : 1005 - 1009
  • [9] Performance Enhancement of GNSS/MEMS-IMU Tightly Integration Navigation System Using Multiple Receivers
    Zhu, Zhenshu
    Jiang, Changhui
    Bo, Yuming
    [J]. IEEE ACCESS, 2020, 8 : 52941 - 52949
  • [10] An Improved Adaptive Kalman Filter for a Single Frequency GNSS/MEMS-IMU/Odometer Integrated Navigation Module
    Yan, Peihui
    Jiang, Jinguang
    Zhang, Fangning
    Xie, Dongpeng
    Wu, Jiaji
    Zhang, Chao
    Tang, Yanan
    Liu, Jingnan
    [J]. REMOTE SENSING, 2021, 13 (21)