#br# 基于自适应EKF的BDS/GPS精密单点定位方法

为使精密单点定位(precise point positioning,PPP)获得更短的收敛时间和更高的定位精度,多个导航系统的集成（例如北斗卫星导航系统(Beidou navigation satellite system,BDS)与全球定位系统(global positioning system,GPS)的组合)和更优的定位方法是两种可行选择。针对传统最小二乘(least square,LS)法解算孤立各历元观测量之间的关系以及扩展卡尔曼滤波(extended Kalman filter,EKF)解算先验信息不准的问题,在PPP中,运用自适应扩展卡尔曼滤波(adaptive extended Kalman filter,AEKF)对过程噪声进行调整,以达到对系统状态的最优估计。文章通过实测数据对算法进行了分析和验证,测试结果表明,与传统的EKF算法相比,基于AEKF算法的PPP收敛速度可提高9 min,定位精度可提高33.7%。

BDS/GPS integrated precise point positioning based on adaptive extended Kalman filter

In order to achieve the shorter convergence period and better positioning accuracy for precise point positioning (PPP), the combination of multiple navigation satellite, such as the Beidou navigation satellite system （BDS） and the global positioning system （GPS）, and the better positioning method are two ways to choose. However, since the traditional least square (LS) estimator ignores the temporal correlation of observations, and the extended Kalman filter (EKF) is limited by inaccurate prior information, a novel PPP method based on adaptive extended Kalman filter (AEKF) is used to adjust the process noise with the measurement consistency test. The experiment results show that, compared with traditional EKF solution, the convergence period of PPP based on AEKF can be shorten by 9 minutes, and the accuracy of positioning can be increased by 33.7%.