Tropospheric polynomial coefficients for real-time regional correction by Kalman filtering from multisource data

The tropospheric delay has a significant impact on high-accuracy positioning of the Global Navigation Satellite System (GNSS). Traditional solutions have their weaknesses. First, the estimation of tropospheric delay as a state parameter slows the positioning filter's convergence, especially critical for Precise Point Positioning (PPP). Second, correction-based approaches, including empirical model, meteorological model and GNSS network observations, have their corresponding limitations. The empirical model comprises yearly data-based statistics, which ignores high temporal-variation components, leading to decreased correction accuracy. The meteorological model requires real-time local weather observations. One can enable the network method of the expensive regional infrastructure of GNSS stations, of which performance depends on the rover-network geometry. In this study, we enable a real-time tropospheric regional correction service by polynomial coefficients from the Kalman filtering of multisource data, including the Global Pressure and Temperature 2 wet (GPT2w) model, weather observations from the National Oceanic and Atmospheric Administration (NOAA), and GNSS network observations. After discussing the weighting strategy examined by the regional dataset from Zhejiang Province, we evaluate the performance of the proposed fusion approach with post-processed PPP results as references. We obtained the optimal weightings for the corresponding dataset, and the average accuracy for Zenith Tropospheric Delay (ZTD) is 0.43, and 1.20 cm under static, active, and overall weather conditions, respectively. Compared with the real-time GNSS network ZTD solution, our proposed fusion solution is improved by 48.21%, 55.20%, and 41.70%, respectively. In conclusion, the proposed approach makes the best of three traditional correction-based methods to provide optimized real-time tropospheric service.