基于自适应的单形采样UKF组合导航算法

在SINS/GPS组合导航系统中,传统的无迹卡尔曼滤波 (unscented Kalman filter,UKF)采用对称采样无迹变换(unscented transform,UT),计算量大,而且采样点到中心点的距离会随着状态维数的增加而增大,产生采样的非局部效应。针对以上问题,利用最小偏度单形采样策略降低UKF计算量以提高系统的实时性,采用比例UT变换来解决采样过程中的非局部效应,通过自适应调整比例因子来提高UKF的估计精度。由此引入了一种改进的UKF算法——自适应比例无迹卡尔曼滤波(adaptive scaled unscented Kalman filter, ASUKF)用于SINS/GPS组合导航系统中。仿真结果表明,这种方法计算量小且精度较高。

UKF integrated navigation algorithm based on adaptive simplex sampling

In the SINS / GPS integrated navigation system, the traditional unscented Kalman filter (UKF) uses the symmetric sampling unscented transform (UT), which can not meet the real time requirement, and the distance from the sampling point to the center point will be increased as the number of state dimension increases, resulting in sampling of non-local effects. To solve the above problems, the minimum skewness simplex UKF reduced sampling strategy is used to improve the real time performance of the system. Use the proportion of UT transformation to solve the nonlocal effects in the process of sampling, by adaptively adjusting the scaling factor to improve the estimation precision of the UKF. This paper introduces an improved UKF algorithm, adaptive scaled unscented Kalman filter (ASUKF) for SINS / GPS integrated navigation system. The simulation results show that this method has low computational complexity and high accuracy.