基于改进UKF的GPS非线性动态滤波研究

A GPS Nonlinear Dynamic Filter Algorithm Based on an Improved Unscented Kalman Filter

为了提高动态定位精度,将一种改进的UKF(Unscented Kalman Filter)算法应用在GPS非线性动态定位解算中。将UKF算法与IEKF(Iterated Improved Kalman Filter)算法相结合,因此保持了基本UKF算法易于实现和收敛速度快的优点,同时由于滤波值是通过迭代扩展的卡尔曼滤波机制得到,进而更新值能更准确的逼近非线性系统状态概率密度函数,具有更高的精度。应用于GPS非线性动态滤波定位中,仿真结果表明:与UKF算法相比,算法能够明显提高定位精度。

In order to increase the positioning accuracy, an improved UKF algorithm is proposed in this paper for applying to GPS nonlinear dynamic filter. This algorithm integrates UKF algorithm with IEKF algorithm so it can basically keep the characters of UKF algorithm-easy to be realized and faster convergence, and the theory of iteration can approach the nonlinear system state probability density function more accurately. Finally the results also have higher accuracy. After applying this improved UKF algorithm to GPS nonlinear dynamic filter, the simulation results show that the positioning accuracy is increased obviously compared with that of UKF algorithm.