# New Satellite Selection Approach for GPS/BDS/GLONASS Kinematic Precise Point Positioning
## 4 Test and Analysis
### Positioning Accuracy of the NSS Model
#### 4.2.2 The Measured Data  
In order to prove the reliability of the proposed algorithm in more detail, GPS/BDS/GLONASS undifferenced and uncombined kinematic PPP was realized with the measured data. In this experiment, the results of GPS PPP with ambiguity resolution at static model were used as a reference truth value. Figure 10 shows the satellite number and GDOP of different satellite selection solutions. In Figure 10, the average satellite number of the original PPP model, NSS model, and optimal model is 26, 12, and 12, respectively. The average percentages of the number of selected satellites with respect to all visible satellites for the NSS model and optimal model is 46.2% and 46.8%. It means that less than half of the visible satellites are selected and the NSS model has the same average satellite number with the optimal model. For GDOP, the average values of the original PPP, NSS model, and optimal DOP algorithm are 1.16, 1.74, and 1.47, respectively. The NSS model fully meets the requirements for general kinematic positioning, such that the satellite GDOP of the position should be less than five [19]. To obtain the accurate position accuracy of kinematic PPP, we also calculated the RMS of the position residuals based on the results after 10 min convergence in Figure 11. It can be calculated that the RMS of the position residuals of original PPP was 1.6 cm in the E direction, 1.7 cm in the N direction, and 4.2 cm in the U direction. Additionally, the RMS of the position residuals was 8.1 cm in the E direction, 2.3 cm in the N direction, and 6.5 cm in the U direction with the NSS model. For the optimal DOP algorithm, the RMS of the position residuals was 4.5 cm in the E direction, 4.3 cm in the N direction, and 7.6 cm in the U direction. Thus, the RMS of the position error is 4.8, 10.6, and 9.8 cm for the original solution, NSS model, and optimal DOP algorithm, respectively. Due to the influence of multipath and cycle slips, the positioning accuracy of the measured data is not as good as that of the MGEX data. However, we can know that the GPS/BDS/GLONASS kinematic PPP \begin{table}
\begin{tabular}{c c c c} \hline  & **Original PPP** & **NSS Model** & **Improved** \\ \hline DARW & 130.440 & 62.823 & 51.8\% \\ GMSD & 78.570 & 45.342 & 42.3\% \\ KARR & 112.436 & 62.214 & 44.7\% \\ MRO1 & 103.957 & 60.057 & 42.2\% \\ XMIS & 112.781 & 60.924 & 46.0\% \\ Mean & 107.637 & 58.272 & 45.9\% \\ \hline \end{tabular}
\end{table} 
Table 4: The computing time of daily data processing using the original PPP and NSS model, respectively (s). \begin{table}
\begin{tabular}{c c c c c c c} \hline  & **DARW** & **GMSD** & **KARR** & **MRO1** & **XMIS** & **Mean** \\ \hline Original PPP & 100\% & 100\% & 100\% & 100\% & 100\% & 100\% \\ NSS model & 46.0\% & 48.3\% & 45.0\% & 45.4\% & 43.0\% & 45.5\% \\ Optimal DOP & 45.8\% & 51.1\% & 45.4\% & 46.8\% & 43.9\% & 46.6\% \\ \hline \end{tabular}
\end{table} 
Table 2: The average percentages of the number of selected satellites with respect to all visible satellites. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & **DARW** & **GMSD** & **KARR** & **MRO1** & **XMIS** & **Mean** \\ \hline Original PPP & 100\% & 100\% & 100\% & 100\% & 100\% & 100\% \\ NSS model & 46.0\% & 48.3\% & 45.0\% & 45.4\% & 43.0\% & 45.5\% \\ Optimal DOP & 45.8\% & 51.1\% & 45.4\% & 46.8\% & 43.9\% & 46.6\% \\ \hline \end{tabular}
\end{table} 
Table 3: The RMSs of position errors at five MGEX stations (cm). with the NSS model has a similar accuracy to the optimal DOP algorithm solution when it is used in practical application. Additionally, the computing time of data processing using the original PPP and NSS model solution is 873.007 and 509.938 s, respectively. Thus, it is beneficial to the application of PPP in low-cost GNSS receivers because the NSS model has better performance on the computational complexity and redundant information.