GNSS多系统PPP中强跟踪自适应Kalman滤波的应用

Application of strong tracking adaptive Kalman filter in GNSS multi-system PPP

精密单点定位(precise point positioning, PPP)技术由于操作简单、定位精度高,现已广泛应用于许多领域。针对PPP解算过程中周围环境改变可能带来的观测噪声和多路径效应,传统滤波算法无法解决其导致的精度下降的问题,本文提出一种强跟踪自适应Kalman滤波(strong tracking adaptative Kalman filtering, SAKF)算法,通过引入渐消因子调整预测误差值,同时使用IGGⅢ函数方法重构测量噪声协方差,从而实现PPP解算。实验结果表明,在静态解算时,SAKF定位精度较传统算法提升约20%,在仿动态解算时,SAKF定位精度提升约55%~60%,同时具有更好的收敛稳定性。

Precise point positioning(PPP)technology has been widely used in many fields because of its simple operation and high positioning accuracy.Aiming at the observation noise and multipath effect that may be caused by the change of surrounding environment,the traditional filtering algorithm cannot solve the problem of precision decline caused by it,this paper proposes a strong tracking adaptive Kalman filtering(SAKF)algorithm.The fading factor is introduced to adjust the prediction error value,and the measurement noise covariance is reconstructed by IGGⅢfunction method,to achieve realize PPP solution.The experimental results show that the positioning accuracy of SAKF is improved by about 20%compared with the traditional algorithm in static solution,and it is improved by about 55%~60%in quasi-dynamic solution,and it has better convergence stability.