## 3 Results and Discussion  
### Preliminary Analyses (Test A)  
In Test A, a permanent GNSS station stores measurements continuously for a month (July 2018), from triple constellations: GPS, GLONASS, and Galileo. In Table 1 and in Figure 7, the visibility during the data collection is resumed. In Table 1, the minimum, the maximum and the mean number of visible satellites are reported, for each considered GNSS combination. The GPS constellation provides the best coverage, with 5 minimum, 12 maximum, and 8 average number of visible satellites. Fewer available satellites characterize GLONASS and Galileo; in fact, for both the mean number is 5. A significantly increased visibility is demonstrated by the double constellations, GPS+GLONASS, GPS+Galileo, GLONASS+Galileo; in particular, the GPS+GLONASS show the best visibility, with minimum, maximum, and mean number of visibility, respectively, 11, 12, and 15. In the triple GNSS configuration, GPS+GLONASS+Galileo, the visibility is further improved with a minimum of 14 available satellites, a maximum of 22, and a mean value of 17. The maximum number of visible satellites, for each day of the dataset and for each GNSS combination, is shown in Figure 7.  
Performance of Multi-GNSS Static PPP Survey with Saastamoinen and Estimate ZTD Tropospheric Models (Test A)  
In this section, the results of Test A are shown. In details, measurements from the considered GNSS combinations are processed in PPP mode, using the Saastamoinen and Estimate ZTD tropospheric models. The performance of the several configurations are compared in terms of precision and position error. Each of the 31 days is separately processed, obtaining the \"daily solutions\"; the \"final solution\" is the mean of the daily solutions and is compared with the known position of the station for error computation. The standard deviations of the daily solutions are also computed, in order to obtain information about the solution stability and precision. In Table 2, the standard deviations \(\sigma\) of the considered configurations are shown. It is evident that the \(\sigma\) values are large, above all in the configurations including GPS, with both Saastamoinen and Estimate ZTD models. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**GNSS Combination** & **Min** & **Mean** & **Max** \\ \hline GPS & 5 & 8 & 12 \\ GLONASS & 4 & 5 & 7 \\ Galileo & 4 & 5 & 8 \\ GPS+GLONASS & 11 & 12 & 15 \\ GPS+Galileo & 9 & 12 & 15 \\ GLONASS+Galileo & 6 & 10 & 13 \\ GPS+GLONASS+Galileo & 14 & 17 & 22 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Number of satellites. Figure 7: Number of maximum available satellites for each day of the dataset and for each Global Navigation Satellite System (GNSS) combination. DOY: Day of Year; GPS: Global Positioning System. In Figure 8a, the X coordinates of the daily solutions, obtained processing GPS-only measurements with Estimate ZTD (black dots) and Saastamoinen (green dots) models, are represented. An anomalous solution, significantly different from the others, is evident at DOY 190, for both the tropospheric models. Further similar situations, relative to the other coordinates and to the other GNSS combinations, have been found. The RAIM-FDE algorithm of RTKLib did not detect outliers in measurements domain; nevertheless, the estimation process provides such anomalous solutions that could negatively affect the final solution. For this reason, an outlier rejection strategy in position domain is necessary too. The outlier rejection strategy, adopted to improve the stability of the positioning, consists in discarding the daily solutions outside the \(6\sigma\) interval centered at the final solution [50; 51]. The X