Accounting for Observation Data Quality in Station Selection for UPD Estimation

Targeting the issues of uneven spatial distribution and significant variations in observation data quality among Multi-GNSS Experiment (MGEX) stations, this paper proposes an adaptive station selection method for Uncalibrated Phase Delay (UPD) estimation that incorporates observation data quality, overcoming the limitations of traditional methods which often overlook station geometry and data quality. A Position Dilution of Precision (PDOP) and UPD error propagation model is developed. Using marginal benefit theory, the optimal number of stations is determined. A multi-indicator evaluation system based on Dempster-Shafer (D-S) evidence theory is established to assess data quality, enabling a dynamic grid algorithm that balances spatial geometry and data quality. Experimental results demonstrate that the proposed method selects 80 optimal stations, accounting for only 30% of the global stations. The estimated narrow-lane UPD products achieve an accuracy better than 0.05 cycles, with a discrepancy of less than 0.002 cycles compared to the full-station solution, indicating comparable precision. Furthermore, the computational time is reduced by 54.1%.