A new satellite selection criterion and strategy based on near real-time accuracy

With the continuous development and improvement of global navigation satellite systems (GNSS), over a hundred navigation satellites now provide precise and reliable positioning services to various types of users. Most existing satellite selection methods adopt geometric dilution of precision (GDOP) as the selection criterion, which considers only the satellite geometric configuration while neglecting the quality of satellite observations. Consequently, the selected satellite set does not always yield ideal positioning results. To address this issue, we propose a satellite selection criterion that combines the near real-time accuracy of satellite observations with geometric configuration. The near real-time accuracy is estimated based on recent short-term observation data. This method estimates the near real-time accuracy of satellite observations using the Melbourne-Wübbena (MW) combination observation and uses this accuracy to weight and compute the weighted geometric dilution of precision (WGDOP-NRT) for satellite selection. Preliminary experimental results demonstrate that, compared to other criteria, the satellite sets selected using WGDOP-NRT achieve shorter convergence times and higher convergence accuracy in the standard precise point positioning (PPP) model. However, the frequent changes in satellite sets between epochs may negatively affect the positioning performance. To mitigate this issue, we propose an improved satellite selection strategy. If the WGDOP-NRT of the satellite set selected in the previous epoch remains below a predefined threshold in the current epoch, the same satellite set is retained. The method for determining the threshold has also been further discussed. This strategy maintains the geometric configuration and observation quality of the satellite set while enhancing its time correlation in satellite selection. Further experimental results indicate that using WGDOP-NRT and the proposed strategy improves PPP performance in both static and dynamic scenarios.