卫星分群的抗差Kalman滤波在GPS/BDS融合精密单点定位中的应用

Robust Kalman filtering based on different satellite types and it's application in GPS/BDS precise point positioning

抗差Kalman滤波是控制GNSS动态导航定位中观测异常的有效算法,当应用到GPS/BDS实时动态精密单点定位(Precise Point Positioning,PPP)时,会出现某些历元定位精度甚至不如单一系统定位精度高,这主要是因为同一接收机接收的不同种类卫星观测量的随机特性不同,使得观测量验后残差的分布特性不一致,抗差估计时随机特性不同的观测量验后残差互比,反而对某一系统优质数据也进行了降权,导致定位结果出现偏差,减弱了GPS/BDS融合精密单点定位的优势。针对这一问题,提出了卫星分群的抗差Kalman滤波算法,并应用到GPS/BDS融合精密单点定位中,算法的核心是在每一历元观测数据质量控制时根据卫星类型分类构建方差膨胀因子,给出了算法的实施步骤,最后通过MGEX实测数据进行了验证,结果表明算法应用到GPS/BDS融合精密单点定位中,相较传统的抗差Kalman滤波算法在东、北、天三个方向分别提高了34.6%、33.3%、31.0%,同时表明该算法提高了GPS/BDS融合精密单点定位的可靠性。

A robust Kalman filtering is used to control the abnormal errors of observations in GNSS dynamic navigation and positioning.However, when it is used in combined GPS/BDS dynamic precise point positioning（PPP）, the positioning accuracy may be worse than that of the single system PPP.Since the random characteristics of different types of satellite＇s observables are different, the post-fit residuals of these observables are different either.When the post-fit residuals are used to calculate the equivalent weight factor in robust estimation, the high quality data may be down weighted, leading to the deviation of the positioning result.So the advantage of combining GPS/BDS with PPP is weakened.To solve this problem, the equivalent weight factor calculation based on different satellite types in robust Kalman filtering is proposed and the execution process of the algorithm is given.Finally, the experiment based on MGEX data is carried out.Compared with the robust Kalman filtering, the proposed algorithm improves the positioning accuracy and reliability of GPS/BDS with PPP.The positioning accuracy in ENU directions are increased by 34.6%, 33.3%, and 31.0%, respectively.