## 5 Field Vehicle Test Measurements  
The objective of the measurement campaign is to validate the simulation results obtained in the previous section, and to evaluate the TCAPLL architecture under realistic conditions. The campaign consists of driving a vehicle in urban canyon environments, while capturing GPS signals using an RF front-end, logging IMU measurements using MEMS inertial sensors, and computing a reference trajectory using a tactical grade IMU. The acquired data are post-processed in the laboratory using the TCAPLL architecture to derive a navigation solution. The instrumental setup used is shown in Figure 24 and it is composed of the following parts. The AIRINS navigation grade Georeferencing and Orientation System from IXSEA [27]. The MTi-G Attitude and Heading Reference System (AHRS) from Xsens [28] that contains a MEMS-based IMU, magnetometers, an integrated GPS receiver, a static pressure sensor and a temperature sensor. The Alpha GNSS RTK receiver from JAVAD [29]. The Stereo RF Front-End from NSL [30], which is used to acquire GPS signals in view, then down-convert, digitize and save them on a PC. In addition, multiple GNSS antennas covering the L1/L2 bands are also used. Figure 25 shows the test vehicle mounted with the measurement platform. Figure 24: Setup used for the field vehicle test measurements. Figure 25: Measurement platform mounted on the test vehicle. The reference navigation solution is computed using INS/GPS integration via optimal forward Kalman filtering and backward smoothing. For that, the IXSEA AIRINS INS is combined with the geodetic-grade Javad Alpha L1/L2 GPS rover receiver (sampling at 10 Hz), and a Topcon Hiper Pro L1/L2 GPS base receiver (sampling at 5 Hz). The carrier-phase GPS observations were double-differenced in post-processing to yield high-precision (cm-level) GPS positioning. These have been combined with the inertial observations in a loosely coupled EKF to obtain a high-precision reference navigation solution [31]. The raw inertial measurements used for the TCAPLL-assisted PLL are obtained from the MTi-G MEMS-based IMU. The parameters used on the modelization of its inertial errors were obtained using the Generalized Method of Wavelet Moments (GMWM) [32, 33]. The gyroscope and accelerometer errors on each axis are modeled as a sum of a white noise, a turn-on to turn-on bias and a correlated drift modeled as a Gauss-Markov process. Table 5 shows the values estimated for the error models, which can be considered typical values for low quality MEMS inertial sensors. More details on how these values are obtained can be found in [18]. Two trajectories are taken during the field vehicle test measurements: 'Trajectory 1' from EPFL to Renens center, with a duration of 920 s including two tunnels having durations of 40 s and 10 s, respectively, and 300 s of driving in urban canyon environments, and 'Trajectory 2' on the EPFL campus, with a duration of 430 s including a 30 s drive in an underground parking structure. The INS frequency is set to 100 Hz and the GPS update rate is 1 Hz. The position solution of the TCAPLL in comparison to the reference solution and the solutions from the MTi-G Xsens and the Javad Alpha are plotted in Figures 26-28 for different parts of the two trajectories. Figure 26 shows the position solution from Trajectory 1 during the 40 s tunnel. The TCAPLL continues to provide a solution inside the tunnel which closely follows the reference trajectory, and outperforms the MTi-G Xsens solution. The Javad Alpha does not provide a solution in the tunnel as there is a GPS total outage; therefore the yellow path is a straight line from the entrance to the exit of the tunnel. Figure 27 shows the position solution from Trajectory 1 in Renens center and during the 10 s tunnel. Again, the TCAPLL outperforms the MTi-G Xsens solution and provides a position that is very close to the Javad Alpha GPS receiver. Figure 28 shows the position solution from Trajectory 2 before, during, and after the drive in the underground parking structure.