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。 The trackless Kalman filter (UKF) achieves better filtering accuracy and convergence speed than the extended Kalman filter (EKF) in the state estimation of nonlinear systems (GPS / DR vehicle integrated navigation system). In order to further reduce the number of sampling points and improve the real-time UKF filtering, a group of n + 2 sampling points are constructed to approximate the system state distribution. Monte Carlo simulation shows that RUKF and UKF are consistent in filtering accuracy and convergence speed, and RUKF is more efficient than UKF.