Multi-GNSS Fusion Real-Time Kinematic Algorithm Based on Extended Kalman Filter Correction Model for Medium-Long Baselines

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 that 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.