# New Satellite Selection Approach for GPS/BDS/GLONASS Kinematic Precise Point Positioning
## 4 Test and Analysis
### Positioning Accuracy of the NSS Model
#### 4.2.1 The MGEX Data  
In this study, the daily undifferenced and uncombined observations from five MGEX stations on DOY 239 in 2017 were processed in the kinematic PPP solution to simulate a kinematic environment. The IGS weekly solutions in Solution Independent Exchange (SINEX) format were adopted as the external reference coordinates. As an important indicator of theoretical impact of the observational geometry, the GDOP value of three PPP solutions should be analyzed. For brevity, only the results from the DARW station are presented. Figure 4 is the visible satellites using the original PPP (Left), NSS model (middle), and Optimal DOP algorithm (Right) at the last second. The daily satellite numbers of the NSS model and Optimal DOP algorithm for GPS, GLONASS, and BDS are shown in Figures 5-7, respectively. Due to different satellite selection strategies of the NSS model (Volume, Azimuth, Elevation mask angle) and Optimal DOP algorithm (GODP), the daily selected satellites are not same. Figure 8 shows the time-dependent change of satellite number and GDOP. In Figure 8, the average satellite number of the original PPP model, NSS model, and optimal model is 26, 12, and 12, respectively. If the satellite selection algorithm is not used in the process of PPP, the GDOP less than 1.5 accounts for 100% and the average GDOP value is 1.07. If the proposed NSS model is used to select satellites, the GDOP less than 2.5 accounts for 98.7%, the GDOP less than 2.0 accounts for 88.4%, and the GDOP less than 1.5 accounts for 19.1%. The average GDOP value is 1.72. Moreover, the GDOP of the optimal DOP algorithm is statistically analyzed. The GDOP value less than 2.0 accounts for 99.7%, and the GDOP less than 1.5 accounts for 75.0%. The average GDOP value is 1.43. From the above analysis of the results, it can be seen that the original PPP has the best GDOP average value and the most visible satellites. The number of satellites in the NSS model solution is greatly reduced when the average values of satellite number and GDOP are compared with other solutions. Figure 4: The visible satellites using the original Precise Point Positioning (PPP) (**Left**), NSS model (**middle**), and Optimal dilution of precision (DOP) algorithm (**Right**) at the DARW station. Figure 5: The visible GPS satellite number using the NSS model (**Left**) and Optimal DOP algorithm. (**Right**) at the DARW station (day of year (DOY) 239, 2017). Figure 6: The visible GLONASS satellite number using the NSS model (**Left**) and Optimal DOP algorithm (**Right**) at the DARW station (DOY 239, 2017). Figure 7: The visible BDS satellite number using the NSS model (**Left**) and Optimal DOP algorithm (**Right**) at the DARW station (DOY 239, 2017). Figure 9 shows the position residuals of GPS/BDS/GLONASS kinematic PPP with undifferenced and uncombined observations in the East (E), North (N), and Up (U) directions. In Figure 9, it can be seen that the position residuals of the NSS model and the optimal DOP algorithm are similar, while the original PPP has a better accuracy at most of epochs, as the original PPP solution uses all visible satellites. To obtain the accurate position accuracy of kinematic PPP, we calculated the RMS of the position residuals based on the results after 10 min convergence. It can be known that the RMS of the position residuals of original PPP was 2.8 cm in the E direction, 1.1 cm in the N direction, and 1.9 cm in the U direction. The RMS of the position residuals was 2.9 cm in the E direction, 2.1 cm in the N direction, and 6.9 cm in the U direction with the NSS model. For the optimal DOP algorithm, the RMS of the position residuals was 6.3 cm in the E direction, 5.6 cm in the N direction, and 2.3 cm in the U direction. Thus, the RMS of the position error (\(\sqrt{\Delta e^{2}+\Delta n^{2}+\Delta u^{2}}\)) is 3.6, 7.8, and 8.6 cm for the original solution, NSS model, and optimal DOP algorithm, respectively.