# Characterization of Carrier Phase-Based Positioning in Real-World Jamming Conditions
## 4 Analysis and Results
### Analysis 3: Effects of Using Multiple Constellations  
The purpose of this analysis is to evaluate the benefits of using more GNSS constellations. With some general observations made in the first two analyses, for the remainder of the article, the focus will be exclusively on carrier phase-based positioning in order to investigate how the best PPK solutions are achieved during jamming incidents. To determine how to achieve the best PPK positioning solution, we first look at the significance of including several GNSS constellations. The following three analyses (3, 4, and 5) make use of the Inertial Explorer (IE) software. In IE, the GPS constellation is mandatory, thus only the impact of adding more constellations to a GPS solution is examined. Clearly, the best robustness would be achieved by determining a solution using only constellations unaffected by jamming. Tables 5 and 6 show position standard deviations using different combinations of constellations for jamming Cases 1 and 2. Interestingly, it is seen in both cases that including more constellations does not always result in a better solution. This is seen in both Cases 1 and 2. In Case 1, the addition of Galileo to the GPS + GLO solution leads to a worse result, while in Case 2, the same is true for the inclusion of GLONASS satellites. \begin{table}
\begin{tabular}{c c c} \hline \hline _Standard Deviations_ & **Clean Period (19:40–19:52)** &
\begin{tabular}{c} **Jamming Period** \\ **(20:00–20:12)** \\ \end{tabular} \\ \hline
**DGPS** & 0.244 m & 0.451 m \\
**PPK float** & 0.004 m & 0.003 m \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Standard deviation for the differential GPS (DGPS) and PPK float solutions using GPS L1 signals from selected GPS satellites (G07, G10, G16, G20, G21, G26, and G27) during the jamming incident and during a similar jamming free period leading up to the incident. Figure 10: Solution 3D error (**top**) and number of satellites in the solution (**bottom**) for various L1 only solutions to Case 1, baseline 1 (11 km). Timestamps on the bottom plot refer to the following events: 11:30:46: Re-inclusion Galileo satellite in the 3 CS after it has reverted to a GPS + GLO solution at the start of the jamming incident—consequences for the positioning solution are shown in the zoom on the top plot.11:49:02: Owing to signal tracking being prevented, a GPS + GAL solution becomes impossible. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Case 1, Baseline 1** & **East [m]** & **North [m]** & **Up [m]** \\ \hline \hline GPS & 0.1540 & 0.1366 & 1.0834 \\ GPS + GLO & 0.0040 & 0.0148 & 0.0149 \\ GPS + \(Gal\) & 0.1412 & 0.0687 & 0.6971 \\ GPS + \(GLO\) + \(Gal\) & 0.0314 & 0.0195 & 0.0801 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Position standard deviation in east, north, and up direction during the jamming incident of Case 1, baseline 1 (11 km)—the full 3D positioning error is shown in Figure 10. The solution is based entirely on L1 phase data. The solution with the least error and largest errors are highlighted in green and red, respectively. Looking at Figures 10 and 11, however, gives a fuller picture of the consequences of including additional constellations. Figure 10 shows that there are benefits of using multiple constellations, as including GLONASS almost completely negates the impact of jamming, owing to the fact that this constellation is not affected by the jamming incident. Including the heavily jammed Galileo constellation to a solution using only GPS also improves the result; it makes a solution possible in the period around 11:20-11:40, which otherwise was prevented using only GPS, and a clear improvement is further seen up until 11:49. Now we turn to how, in both Case 1 and Case 2, two-constellation solutions (2 CS) seem to perform better than a full three-constellation (3 CS) solution. One could suspect