MIEKF优化算法在GPS定位测速中的应用

Application of MIEKF Optimization Algorithm in GPS Positioning and Velocity Measurement

为了提高GPS定位测速的精度,引入了迭代扩展卡尔曼滤波(IEKF)算法。针对上述算法存在受状态初值影响的问题以及在迭代过程中存在稳定性不足的缺点,提出基于最小二乘(LS)和Levenberg-Marquardt(L-M)方法的改进迭代扩展卡尔曼滤波(MIEKF)优化算法。先用LS算法对用户接收机初始状态进行估算,然后在IEKF算法解算过程中利用L-M方法调整预测协方差矩阵,以保证算法具有全局收敛性,最后采用GPS星历数据进行仿真对比实验。通过对仿真结果的分析,证明MIEKF优化算法能够提高GPS定位测速精度,改善收敛性能。

In order to improve the accuracy of GPS positioning and velocity measurement, an algorithm named h- erative Extended Kalman Filter （IEKF） is introduced in this paper. To solve the problem that the IEKF is influence of the initial iterative value and lack of stability, a Modified Iterative Extended Kalman Filter （MIEKF） optimization algorithm based on Least Square （IS） method and Levenberg-Marquardt （L-M） method is proposed. Firstly, the initial state values of the receiver were estimated with the LS method. Then during the process of IEKF algorithm, the covariance matrix was adjusted with L-Mmethod to ensure global convergence. Finally, GPS ephemeris data were used for simulation verification. Based on the analysis of the simulation results, it was proved that the MIEKF optimi- zation algorithm can improve the accuracy of GPS positioning and velocity measurement, and it demonstrates better convergence performance.