一种快速GPS/DR组合导航系统状态估计算法研究(英文)

Real-time improvement in state estimation for GPS/DR integrated navigation system

无轨迹卡尔曼滤波(UKF)技术在非线性系统(GPS/DR车载组合导航系统)的状态估计中取得了比扩展卡尔曼滤波(EKF)更好的滤波精度和收敛速度。为了进一步减少采样点数目,提高UKF滤波实时性,一组n+2个采样点被构造用于逼近系统状态分布。蒙特卡洛仿真表明RUKF和UKF在滤波精度和收敛速度上是一致的,RUKF的计算效率好于UKF。

This paper deals with the application of Unscented Kalman Filter(UKF),which has a better filtering precision and convergence rate than Extended Kalman Filter (EKF) for nonlinear system,to a GPS/DR integrated navigation system.In order to reduce the number of sigma points and increase the real-time in UKF,a set of(n+2) sigma points is constructed to approximate the probability distributions of the system states.The Monte-Carlo simulation results show that the Reduced-sigma points UKF(RUKF) has a higher computational efficiency than the UKF,and an accordant filtering precision and convergence rate with the UKF.