## 5 Field Vehicle Test Measurements
In this case, the TCAPLL continues to provide a position solution, however the drift with time is higher than in the tunnels as the dynamics are different. It can be noted that after each 90 \({}^{\circ}\) turn, the position solution degrades. Here also, the TCAPLL outperforms the MTi-G Xsens solution. The Javad Alpha receiver does not provide any solution in the parking structure as there is a GPS total outage. The reason the TCAPLL architecture outperforms the MTi-G Xsens is twofold. First, the GMWM provides better modeling of the gyroscope and accelerometer \begin{table}
\begin{tabular}{c c c} \hline  & **Gyro** & **Accelerometer** \\ \hline White noise (1\(\sigma\)) & 240 deg/h/\(\sqrt{\text{Hz}}\) & 2 mg/\(\sqrt{\text{Hz}}\) \\ Turn-on bias (1\(\sigma\)) & 3260 deg/h & 50 mg \\ GM correlation time [s] & 350 & 30 \\ GM 1\(\sigma\) noise & 3 deg/h/\(\sqrt{\text{Hz}}\) & 0.024 mg/\(\sqrt{\text{Hz}}\) \\ \hline \end{tabular}
\end{table} 
Table 5: Estimated error models of the MTi-G IMU for each axis. errors, and consequently during GPS total outages when there is total dependence on the inertial measurements, the TCAPLL has far better performance. Second, the integration scheme in the MTi -G is loosely coupled; however, the TCAPLL has tight integration with PLL assistance and a monitoring system to check the quality of the inertial measurements. The position errors of the TCAPLL during Trajectory 1 and Trajectory 2 are shown in Figure 29, in comparison with the stand-alone GPS solution. The performance of the TCAPLL architecture is better, and this is also shown in Tables 6 and 7 where the standard deviation and mean of the velocity, position, and attitude errors are shown for both trajectories. The TCAPLL velocity error is reduced by 88% and 77%, and the position error by 70% and 42%, respectively, for Trajectory 1 and Trajectory 2. The smaller improvement in Trajectory 2 is due to the fact that the measurements were made mainly in good GPS conditions, for which the stand-alone GPS performs relatively well. Figure 27: Position solution in Renens center and during a 10 s tunnel under the railway tracks. Figure 26: Position solution during the 40 s tunnel in the trajectory from EPFL to Renens center. The uncertainty of the feed-forward component in both trajectories is shown in Figure 30. When there are GPS updates, the uncertainty remains low and in both cases it is less than 5 Hz. This uncertainty is small enough to allow increasing the coherent integration time up to 10 ms without jeopardizing the robustness of the PLL (see Figure 4). However, when there are GPS total outages, the uncertainty increases drastically. The i increase varies between each satellite, and is inversely proportional to the satellite elevation, _i.e._, the higher the elevation of the satellite, the smaller the increase of its feed-forward frequency uncertainty during GPS total outage. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline  & \multicolumn{4}{c}{**Velocity Error [m/s]**} & \multicolumn{4}{c}{**Position Error [m]**} \\ \hline  & & GPS & \multicolumn{2}{c}{TCAPLL} & \multicolumn{2}{c}{GPS} & \multicolumn{2}{c}{TCAPLL} \\ Trajectory & 1 & 2 & 1 & 2 & 1 & 2 & 1 & 2 \\ \(\sigma\) & 3.213 & 1.995 & 0.382 & 0.436 & 11.831 & 4.873 & 3.556 & 2.787 \\ mean & 0.056 & 0.109 & 0.096 & 0.034 & 6.649 & 4.505 & 2.536 & 2.879 \\ \hline \end{tabular}
\end{table} 
Table 6: Standard deviation and mean of the GPS and TCAPLL velocity and position errors. Figure 28: Position solution during the 30 s trajectory in the underground parking structure. Figure 29: Position error during Trajectory 1 (**left**) and Trajectory 2 (**right**). For both trajectories the uncertainties of the feed-forward components during the outages are still acceptable for re-tracking the GPS signals once they reappear, without re-acquisition.