Abstract: Multipath effects in global navigation satellite system (GNSS) observations are environment-dependent and cannot be eliminated or mitigated through differential methods. They significantly impact the accuracy of GNSS data processing and service performance. The BeiDou system comprises satellites in high, medium, and low Earth orbits. To mitigate the impact of orbit repetition differences among satellites, the multi-point hemispherical grid model (MHGM) based on spatial domain modeling presents an effective solution. However, due to the MHGM method's high number of parameters for estimation and its demand for substantial computational resources such as memory and CPU, it is generally unsuitable for modeling multipath errors in large-scale reference station networks. This paper investigates a partitioning modeling method based on public stations, achieving rapid and effective modeling of multipath errors in large-scale reference station networks. The effectiveness of this approach is validated using observation data from 221 reference stations of the BeiDou ground-based augmentation system in Guangdong province. Test results demonstrate a service performance enhancement of 11.2% for the existing system in Guangdong province, without requiring additional hardware installation. This research holds scientific and engineering significance in improving the accuracy and reliability of data processing services in the BeiDou system.

Key words: BeiDou system, large-scale reference stations, multipath errors, multi-point hemispherical