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Vehicle sideslip angle estimation by fusing inertial measurement unit and global navigation satellite system with heading alignment

机译:通过融合惯性测量单元和具有标题对齐的全球导航卫星系统的车辆侧线角估计

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摘要

Estimation of the sideslip angle is significant for vehicle safety control systems such as electronic stability control. This paper proposes a vehicle-kinematic-model-based sideslip angle estimation method by fusing the information from an inertial measurement unit (IMU) and global navigation satellite system (GNSS) with aligning the heading from the GNSS. To estimate the velocity and attitude errors of the reduced inertial navigation system (R-INS), we first formulate the associated system error dynamics. Then, to further improve the heading estimation accuracy of the R-INS, the heading from the GNSS is aligned to the vehicle longitudinal direction by a robust regression method and adopted to estimate the heading error of the R-INS. Next, an adaptive Kalman filter is applied to estimate the errors in the R-INS to attenuate the noise influence. With the velocity in navigation coordinates and the attitude between the navigation coordinates and vehicle body coordinates from the R-INS, the velocity and sideslip angle in the vehicle body coordinates are computed. Finally, tests in straight line, double lane change (DLC), and slalom maneuvers are performed to verify the sideslip angle estimation and the heading alignment method. After aligning the heading from the GNSS, the sideslip angle estimation accuracy is improved, and the mean error under typical DLC and slalom maneuvers are below 0.21°.
机译:对于诸如电子稳定性控制的车辆安全控制系统,偏斜角的估计是显着的。本文提出了一种基于车辆运动学模型的偏光角估计方法,通过融合来自惯性测量单元(IMU)和全局导航卫星系统(GNSS)的信息,使来自GNSS的标题对准。为了估算减少惯性导航系统(R-INS)的速度和姿态误差,我们首先制定相关的系统错误动态。然后,为了进一步提高R-Ins的前置估计精度,通过稳健的回归方法将来自GNS的标题与车辆纵向对齐,并采用以估计R-INS的标题误差。接下来,应用自适应卡尔曼滤波器来估计R-INS中的错误以衰减噪声影响。利用导航坐标和导航坐标和车身之间的姿势从R-INS之间坐标,计算车身坐标中的速度和侧滑角。最后,执行直线,双车道变化(DLC)和障碍臂操作以验证侧滑角估计和标题对准方法。在从GNSS对齐标题之后,侧滑角估计精度得到改善,典型DLC和障骨架机动下的平均误差低于0.21°。

著录项

  • 来源
    《Mechanical systems and signal processing》 |2021年第3期|107290.1-107290.18|共18页
  • 作者单位

    School of Automotive Studies Tongji University Shanghai 201804 China Institute of Intelligent Vehicles Clean Energy Automotive Engineering Center Tongji University Shanghai 201804 China;

    School of Automotive Studies Tongji University Shanghai 201804 China Institute of Intelligent Vehicles Clean Energy Automotive Engineering Center Tongji University Shanghai 201804 China;

    School of Automotive Studies Tongji University Shanghai 201804 China Institute of Intelligent Vehicles Clean Energy Automotive Engineering Center Tongji University Shanghai 201804 China;

    School of Automotive Studies Tongji University Shanghai 201804 China Institute of Intelligent Vehicles Clean Energy Automotive Engineering Center Tongji University Shanghai 201804 China;

    School of Automotive Studies Tongji University Shanghai 201804 China Institute of Intelligent Vehicles Clean Energy Automotive Engineering Center Tongji University Shanghai 201804 China;

  • 收录信息
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Sideslip angle estimation; Heading alignment; Velocity estimation; Robust regression; Kalman filter; Information fusion;

    机译:SideLip角度估计;前进的对齐;速度估计;强大的回归;卡尔曼滤波器;信息融合;

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