NHC lever arm estimation algorithm for vehicle-integrated navigation systems based on dead reckoning
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摘要:
车辆运动的非完整性约束(NHC)可用作车载组合导航系统的速度观测信息，能够有效抑制惯性导航系统(INS)的误差累积。充分发挥NHC的约束作用需要准确估计和补偿IMU安装角和NHC杆臂。因此对NHC杆臂进行研究，通过三维航位推算(dead reckoning)及扩展卡尔曼滤波器，在没有里程计的情况下，同时估计IMU安装角和NHC杆臂。实验结果表明，本算法能够同时准确估计高精度惯导和低精度MEMS惯导的安装角和NHC杆臂，安装角估计误差小于0.1°，使用估计的NHC杆臂投影点比IMU中心更符合NHC约束条件，能够明显提高NHC约束的辅助效果，提升车载组合导航系统的性能。
Abstract:The non-holonomic constraint (NHC) of vehicle motion can be used as the velocity observation information for the vehicle-integrated navigation system, which can effectively suppress the error accumulation of the inertial navigation system (INS). To fully exert the constraint function of NHC, it is significant to accurately estimate and compensate for the inertial measurement unit (IMU) stagger angle and NHC lever arm. This paper researched the NHC lever arm and estimated the IMU stagger angle and NHC lever arm simultaneously by three-dimensional dead reckoning and Kalman filter without an odometer. The results show that the proposed algorithm can accurately estimate the stagger angle and NHC lever arm of the high-precision INS and low-precision micro-electro-mechanical system (MEMS) INS, and the stagger angle error is less than 0.1°. The estimated NHC lever arm projection point is more in line with the NHC constraint condition than the IMU center, and it can significantly improve the auxiliary effect of NHC constraint and strengthen the performance of the vehicle-integrated navigation system.
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图 2 IMU安装角和NHC杆臂估计算法框图
Figure 2. Block diagram of IMU stagger angle and NHC lever arm estimation algorithm
图 5 NHC杆臂估计曲线（来自实验A数据）
Figure 5. NHC lever arm estimation curve(from the data in experiment A)
图 6 IMU安装角估计曲线（来自实验A数据）
Figure 6. Estimation curve of IMU stagger angle(from the data in experiment A)
图 7 参考系统（高精度惯导POS620）的y轴速度（来自实验A数据）
Figure 7. y-axis velocity of reference system (high-precision INS POS620) (from the data in experiment A)
图 8 参考系统（高精度惯导POS620）的z轴速度曲线（来自实验A数据）
Figure 8. z-axis velocity of reference system (high-precision INS POS620) (from the data in experiment A)
图 9 MEMS IMU（ADIS16465）姿态误差曲线（来自实验A数据）
Figure 9. Attitude error curve of MEMS IMU (ADIS16465)(from the data in experiment A)
图 10 MEMS IMU（HG101）姿态误差曲线（来自实验A数据）
Figure 10. Attitude error curve of MEMS IMU (HG101)(from the data in experiment A)
表 1 IMU基本参数
Table 1. Basic parameters of IMU
IMU型号 角度随机游走/
((°)·h−1/2)速度随机游走/
((m·s−1)·h−1/2)陀螺零偏/
((°)·h−1)加表零偏/
(m·s−2)PO620 0.03 0.003 0.01 0.000 15 ADIS16465 0.10 0.100 50.00 0.000 50 HG101 0.30 0.200 15.00 0.002 00 
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表 2 NHC杆臂估计值
Table 2. Statistics of estimated values of NHC lever arm
m 实验组别 x轴分量 y轴分量 POS620 ADIS16465 HG101 POS620 ADIS16465 HG101 A 0.412 0.560 0.777 0.000 0.015 −0.016 B 0.396 0.556 0.774 −0.002 0.017 −0.018 C 0.416 0.573 0.793 0.020 0.022 −0.013
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表 3 NHC杆臂X轴分量估计值横向对比结果
Table 3. Lateral comparison results of estimated values of X-axis component of NHC lever arm
m 实验组别 x轴分量 POS620 ADIS16465 HG101 A 0.412 0.375 0.397 B 0.396 0.371 0.394 C 0.416 0.388 0.413
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表 4 GNSS中断时的位置漂移误差统计
Table 4. Position drift error statistics during GNSS outages
IMU型号 方向 位置发散误差统计值/m 方案1 方案2 方案3 北向 0.525 0.468 0.395 POS620 东向 0.825 0.821 0.607 高程 0.425 0.262 0.266 北向 6.902 5.266 3.464 ADIS16465 东向 10.079 6.205 3.503 高程 1.364 0.621 0.454 北向 11.922 8.145 3.991 HG101 东向 16.476 3.857 3.095 高程 3.730 0.614 0.387
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