# Adaptive Navigation Based on Multi-Agent Received Signal Quality Monitoring Algorithm
## 4 Results and Discussion  
In order to precisely estimate the degradation in navigation performance, we analyze the variations in GNSS measurement parameters in various environments and establish the cause-effect relationships. Rigorous field experimentation is carried out in dynamic (moving) and static modes having a wide range of operating environments. Figure 9a shows the satellite availability, which represents the visible satellites tracked by the GNSS receiver. Figure 9b presents the Position Dilution of Precision (PDOP) values,  
Figure 8: Fuzzy logic memebrship functions. and the position error is plotted in Figure 9c. As mentioned above, the experimental route contains the typical environments, i.e., urban, sub-urban having high-roof buildings, bridges, traffic, and clear open sky with minimal degradation. It is observed that in a dynamic mode, due to a rapidly changing environment, fundamental GNSS measurement parameters change frequently. In the P1 observation period of 10 min, on average, 37 satellites were tracked by the GNSS receiver, having negligible degradation due to a clear route. The clear environment results in good satellite geometry and higher PDOP values. PDOP deviates from 0.75 up to a maximum of 1.17. The best satellite availability and good satellite geometry are the major requirements of highly accurate and precise positioning estimation. Hence, it is noted from the P1 observation window that the mean position error is 1.3 m due to the best satellite geometry. As the route drives through a complex bridge sub-urban environment with high-rise walls, GNSS measurements vary greatly. During the observation time, P2 of 14-22 min visible satellites decreased to 27, which disturbs the satellite geometry due to MP/NLOS receptions. It can be noted from the figure that PDOP rises to 2.21 and varies from 1 to 2 during the complete observation period. These values are not too high, but it has significant effects on positioning estimation, which is observed from the position error. At the mean position, PDOP rises to 4.14 and reaches a maximum of 7.8 with some peaks at different times. We can say that there are frequent disruptions due to MP/NLOS in the P2 window. The advancements in the GNSS have enhanced the visible satellites, but MP is still the most prominent error source for GNSS positioning, as shown in Figure 9.  
In the observation window of P3, we observed significant disturbances due to complicated building structures in highly urbanized environments. Due to the complex surroundings of the environment, we observed an excessive amount of uncertainties and inaccuracies in GNSS measurement parameters. Though the mean visible satellites were 27, we observed consequences during the observation window from 37 to 62 min, including MP and NLOS receptions, which resulted in larger PDOP values and unfavorable satellite geometry. We have seen some higher peaks in PDOP that go above 3 at a certain time in the P3 route. During the complete P3 route, there are drastic variations in the PDOP, leading to a sharp increase in position error. It is noted that position error rises to 14.3 m and the estimated error mean is 5.3 m in the P3 window. Overall, we have seen the worst performance of GNSS in the highly urbanized environment and we can say that MP/NLOS represent the primary error contributor in this severe environment. In the last observation window P4, we have seen a similar performance of GNSS parameters (Visible Satellites, PDOP and Position Error) as in P1, as both scenarios represent the clear open sky environment. Figure 9: Positioning performance comparison of multi-constellation GNSS in dynamic (moving) mode. (**a**) Satellite Availability, (**b**) PDOP and (**c**) Position Error (m). The abruptly deviating environment during the dynamic mode makes the GNSS more difficult to realize. As noted from Figure 9, recent advancements have enhanced the satellite availability of the GNSS in terms of the number of satellites.