一种基于因子图的RIMU/GNSS组合导航算法
An Integrated Navigation Algorithm of RIMU/GNSS Based on Factor Graph
胡任祎 1史丽楠 1崔莹莹 1贺彦峰 1陈平1
作者信息
- 1. 北京航天自动控制研究所,北京 100854
- 折叠
摘要
针对传统基于滤波方法的RIMU/GNSS组合导航系统因载体大幅度机动条件下滤波结果不稳定的问题,提出了一种基于因子图的估计方法.建立面向冗余惯性导航系统的状态方程与量测方程,并将冗余惯性数据融合至载体坐标系三轴.建立基于因子图的数据融合方法,将融合后的惯性数据与卫星信息抽象为因子节点,状态信息抽象为变量节点,构建包括惯性因子与GNSS因子的代价函数并以非线性优化的方式对状态量进行估计.数字仿真证实,因子图方法能有效降低载体的位置误差,且均方根误差明显小于卡尔曼滤波算法.
Abstract
In order to solve the problem that the filtering result of RIMU/GNSS integrated navigation sys-tem based on filtering method is unstable under the condition of large maneuvering of carrier,an estimation method based on factor graph is proposed.The state equation and measurement equation oriented to the re-dundant inertial navigation system are established,and the redundant inertial data are fused into the carri-er coordinate system three axes.A data fusion method based on factor graph is established,and the fused inertial data and satellite information are abstracted as factor nodes,and the state information is abstracted as variable nodes.The cost function including inertia factor and GNSS factor is established and the state quantity is estimated in a nonlinear optimization way.The reslult of numerical simulation shows that per-formance of using the factor graph method can effectively reduce the position error of the carrier and the root mean square error is obviously degraded than that of the Kalman filter.
关键词
冗余惯组/因子图/信息融合Key words
Redundant inertial measurement units/Factor graph/Information fusion引用本文复制引用
出版年
2024
NETL
NSTL
万方数据