## 3 Testing and Results
### Test-3: Flight in Changing Conditions  
The third test focuses on the behavior of the RTK positioning system under kinematic conditions, meaning a drone that keeps moving after passing through a GNSS-denied spot instead of hovering close to the obstacle. This condition may be representative of a drone surveying a road or a railway and passing under a wide bridge, a short tunnel, or through an urban canyon between buildings. Also, the case of the exit after an indoor flight can resemble this test. As shown in Figure 2 (red dots), the cart carrying the RTK rover receiver followed a circuit passing under the tunnel of the ITC building's south entrance, then flanking the lowest part of the building wall for about 20 m, and finally moving in the parking lot where the sky visibility is almost optimal. For this test, the approximate moving speed of the cart is about 2 m/s. The track was covered 26 times, and Figure 9 shows the plane coordinates recorded during the test. As in the previous tests, the black, blue, and red dots represent fixed, floating, and single-point positioning solutions, respectively, whereas the triangles are used to show the positions where the ambiguity fixing was lost or re-gained. In this case, the receiver lost the fixing in positions a few meters from each other, near the entrance of the tunnel, while a rather bigger variability can be observed in the position where the receiver re-gained the fixing condition.  
In terms of time to re-fix the solutions after having left the tunnel, the main results are shown in Figure 10 and were calculated relative to the instant when the receiver passed on the average position in which the fixing condition was lost approaching the tunnel. These time spans ran from 6 to 35 s, where the latter value was quite an exception; excluding it, the average time was about 11 s with a variability of 4 s RMS.  
Additional tests were performed by varying the time under the tunnel to assess the influence of the time interval without satellite signals. A first attempt was to vary the time spent in an urban canyon or under a bridge to verify its influence on the re-fixing time.  
For this reason, the cart was left under the bridge for a time span of 10 and 30 s for the first and second tens of runs, respectively, while during the last six runs, this time was one minute. These intervals were selected considering a typical operational situation, where the drone has limited flight time, and inspections cannot last too long without dedicated sensors (such as LiDAR) and SLAM algorithms [32; 33] for indoor navigation.  
Figure 8: Statistics related to test-2. The (**left**) histogram reports the time to lose the ambiguity fixing condition starting from the initial position, while the (**right**) one shows the time required to re-gain the re-fixed the solution after having left the obstacle.  
However, these tests did not highlight any relevant changes in the receiver's performance, depending on the time spent in signal-denied conditions, at least for the relatively limited time intervals considered.  
No significant false fixes were detected in test-3, which may indicate that the receiver motion helped the system avoid keeping a wrongly estimated set of initial phase ambiguities to define the fix conditions. This is reasonable, as a set of integer ambiguity values, when correctly estimated, should remain coherent as the receiver position change, while motion emphasizes its incoherence, since both the initial-phase ambiguities and receiver coordinates are unknown for the problem estimated at once.