# PPP and PPP-AR Kinematic Post-Processed Performance of GPS-Only, Galileo-Only and Multi-GNSS
## 3 Results
### Some Station Examples  
The station BRUX from the IGS network has been chosen to show in detail the temporal series for the entire week, as examples of good PPP and PPP-AR repeatability. Figure 2 gives examples for PPP and PPP-AR solutions for Galileo-only, GPS-only and their combinations. For each temporal series the  
Figure 1: Steps of the procedure to perform precise point positioning (PPP) and PPP with phase ambiguity resolution (PPP-AR) of a combined Multi-GNSS solution (GPS + Galileo). mean value is calculated and subtracted to center the series to zero. In addition, Table 3 shows the percentages of ambiguity resolution for the PPP-AR mode. From Figure 2, it is observed that certain irregularities of the repeatability patterns in the PPP solutions do almost disappear in a PPP-AR solution. There is a little jump at the end of DOY 043 for north and east directions coming from the contribution of the GPS system (also seen in GPS-only PPP of around 2 cm in East and 1 cm in North) to the Multi-GNSS solution. Nevertheless, it is seen that in Multi-GNSS PPP-AR this jump is reduced to less than 0.5 cm. The importance of AR in precise positioning is also seen when comparing the PPP solution to the PPP-AR solution. It is seen that some jumps in the PPP solution no longer appear when performing PPP-AR processing. The PPP-AR mode is shown to be smoother and more linear than the PPP mode (in particular for the east direction). Another example is the one seen for Galileo-only around DOY 046. There is a downward jump of around 2 cm for east and north directions that is eliminated for the PPP-AR mode. The 1-\(\sigma\) values of the above temporal series for BRUX and another two examples of stations (CAS1 and NYA) for PPP and PPP-AR are gathered in the following Tables 4-6. \begin{table}
\begin{tabular}{l c c c c c c} \hline \hline
**BRUX AR (\%)** & **042** & **043** & **044** & **045** & **046** & **047** & **048** \\ \hline Galileo & 95.24 & 100 & 100 & 97.67 & 100 & 100 & 95.45 \\ \hline GPS & 98.63 & 91.67 & 91.30 & 98.44 & 100 & 98.48 & 94.03 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Ambiguity resolution percentages for Galileo and GPS system for BRUX station. Figure 2: PPP and PPP-AR solutions for BRUX station for Galileo-only, GPS-only and Multi-GNSS for the period 11–19 February 2019, DOY: 42–49/2019. For Galileo-only and GPS + Galileo, additional biases of +0.05 m and \(-0.05\) m have been added respectively for better representation in the graph. From these examples it is clear that level of accuracy achieved varies for each individual station. This could be because of several parameters such as multipath, station ANTEX parameters (i.e., for Galileo station, antenna ANTEX files are not yet provided so therefore the GPS station antenna ANTEX files were used), etc. Notwithstanding, it is seen that the level of accuracy from Galileo-only solutions is nearly comparable to the one of GPS-only solutions. For the Galileo-only solutions there were less measurements used than for the GPS-only solution due to the fact that the Galileo constellation has less satellites than GPS. It is expected that once the Galileo constellation is complete, the accuracy of PPP and PPP-AR respectively will improve. Ambiguity resolution improves the solution about 1-2 mm in East and North directions (around 10-45% improvement) (Here and elsewhere in the text, improvements in % are computed according to the formula: 100%-\((v_{2}-v_{1})/v_{1}\), where \(v_{1}\) and \(v_{2}\) are the values of before (hence reference) and after the change.) and about 1-2 mm in up direction (around 5-20% improvement). It is observed that the up component is improving less than east and north components when comparing the PPP and the PPP-AR cases using a single GNSS system. This is explained due to the fact that highly correlated parameters: i.e., up component, the tropospheric parameters (i.e., Zenith Tropospheric Delay (ZTD)) and station clocks are better de-correlated in the Multi-GNSS solutions.