GNSS/INS loose combined navigation based on factor graph optimization PPP
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摘要: 针对全球卫星导航系统(GNSS)容易因建筑物遮挡、多路径效应以及卫星可见数不足导致的GNSS信号失锁问题，提出了一种基于因子图优化(FGO)的精密单点定位(PPP)算法进行GNSS和惯性导航系统(INS)的融合定位方法.首先参照经典PPP双频无电离层模型，构建伪距、载波因子，根据非线性优化理论求解非线性最小二乘问题；再将优化后的PPP位置信息作为PPP因子，与地球自转的精化预积分因子一同构建到GNSS/INS松组合FGO框架中，实现组合导航信息非线性优化. 车载实测结果表明：针对PPP，所提算法的定位精度相比扩展卡尔曼滤波(EKF)算法在北(N)方向、东(E)方向、地(D)方向上分别提升37.09%、28.79%、64.59%；针对GNSS/INS组合导航，该算法的定位精度相比EKF算法在三个方向上分别提升了49.08%、41.22%、71.86%.
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关键词:
- 因子图优化(FGO) /
- 扩展卡尔曼滤波(EKF) /
- 精化预积分 /
- 精密单点定位(PPP) /
- 组合导航
Abstract: Aiming at the problem of global navigation satellite system signal loss caused by building occlusion, multipath effect and insufficient satellite visibility, a precise point positioning (PPP) algorithm based on factor graph optimization is proposed for the integrated positioning of GNSS and INS. First, with reference to the classical PPP dual-frequency lonosphere-free model, the pseudo-range and carrier factors are constructed, and the nonlinear least-squares problem is solved according to the nonlinear optimization theory. Then, the optimized PPP location information is used as the GNSS PPP factor, and the refined pre-integration factor considering the rotation of the earth is constructed into the GNSS/INS loose combination factor graph frame, to realize the nonlinear optimization of integrated navigation information. The results of on-board real measurement show that the positioning accuracy of the proposed algorithm is 37.09%, 28.79% and 64.59% higher than that of the extended Kalman filter algorithm in the north, east, and down directions respectively for PPP; For GNSS/INS integrated navigation, the positioning accuracy of the algorithm is 49.08%, 41.22% and 71.86% higher than that of the extended Kalman filter algorithm in three directions. -
表 1 惯性导航元件标定参数及空间杠杆杆臂
设备名 陀螺仪 加速度计 北东地坐标系中空间杠杆杆臂/m 零偏
稳定性
/(deg·h−1)角度随机
游走
/deg·
sqrt(h)−1零偏
稳定性
/(mGal)速度随机
游走
/m·s−1,
sqrt(h)−1HGuidei300 3 0.15 0.02 0.02 0,0.114,
−0.120
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表 2 基于EKF和FGO的PPP的三维位置RMSE
m IMU EKF PPP FGO PPP N E D N E D HGuidei300 0.213 0.257 1.124 0.134 0.183 0.398
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表 3 GNSS/INS三维位置RMSE
m 模型类别 N E D 实验一(EKF PPP-EKF) 0.273 0.296 1.002 实验二(FGO PPP-EKF) 0.185 0.259 0.844 实验三(EKF PPP-FGO) 0.233 0.243 1.015 实验四(FGO PPP-FGO) 0.139 0.174 0.282
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