Abstract: In the case of a medium-long baseline, for real-time kinematic (RTK) positioning, the fixed rate of integer ambiguity is low due to the distance between the base station and the observation station. Moreover, the atmospheric delay after differential processing cannot be ignored. For correcting the residual atmospheric errors, we proposed a GPS/BDS/Galileo/GLONASS four-system fusion RTK positioning algorithm, which is based on the extended Kalman filter (EKF) algorithm. After realizing the spatio-temporal unification of multiple global navigation satellite systems (GNSSs), we introduced a parameter estimation of atmospheric errors based on the EKF model, using the least-squares integer ambiguity decorrelation adjustment (LAMBDA) to calculate the integer ambiguity. After conducting experiments for different baselines, the proposed RTK positioning algorithm can achieve centimeter-level positioning accuracy in the case of medium-long baselines. In addition, the time required to solve the fixed solution is shorter than that of the traditional RTK positioning algorithm.

Key words: real-time kinematic (RTK), extended Kalman filter (EKF), baseline, ambiguity, ionospheric delay, tropospheric delay

摘要： 在中长基线情况下，由于基站与观测站之间的距离较远，实时动态（RTK）定位的整数模糊度固定率较低。此外，差分处理后的大气残差也无法忽略不计。为了修正大气残差，提出了一种基于扩展卡尔曼滤波（EKF）算法的GPS/BDS/Galileo/GLONASS四系统融合的实时动态定位算法。在实现多种全球导航卫星系统（GNSS）的时空统一后，引入了基于扩展卡尔曼滤波模型的大气误差参数估计，并利用最小二乘降相关平差算法（LAMBDA）计算整数模糊度。经过对不同基线进行实验，所提出的实时动态定位算法在中长基线情况下能够实现厘米级的定位精度。此外，求解固定解所需时间比传统实时动态定位算法更短。

关键词: 实时动态，扩展卡尔曼滤波，基线，模糊度，电离层延时，对流层延时