# Adaptive Navigation Based on Multi-Agent Received Signal Quality Monitoring Algorithm
## 3 Methodology
### Field Experimentation and Candidate Sites  
To analyze and quantify the advancement and its impact on navigation performance, we have conducted four experiments on carefully planned and chosen pre-surveyed can  
Figure 3: Workflow of the paper.  
Figure 2: Potential Vulnerabilities of satellite signal reception in urban environment. S1–S4 are satellites in the space from 1 to 4.  
didate sites. The three candidate sites are static and can be found at the Department of Electrical Engineering, Sukkur IBA University, and one experiment was carried out at Sukkur, which is one of the highly dense urban cities in Sindh, Pakistan, in moving mode. Figure 4a is the first candidate site, in which an antenna is placed on the rooftop of the Electrical Department building, with a clear view of the open sky, and it is the best-case environment due to no obstructions in the surroundings. Figure 4b is the second site among the disrupted environments, surrounded by buildings and trees. In this environment, there are frequent possibilities of experiencing multipath and NLOS receptions. Figure 4c illustrates the degraded environment, and prominently shows a large amount of MP and NLOS at the third site, because the antenna is placed in an indoor environment enclosed by side walls. The observation period for all three static scenarios is six hours in order to obtain a comprehensive analysis. For moving mode, we have selected one of the dense cities of Sindh province in Pakistan, that is, Sukkur; it was chosen as a target place for analyzing navigation performance, as shown in Figure 5. This city covers most of the typical scenarios, i.e., sub-urban, urban and clear sky. The route was chosen around 20 km, involving all the typical environments within the city. The complete route for the moving case can be seen in Figure 5a. The clear open sky environment is shown in Figure 5b, where we have little chance of degradation. Figure 5c shows the sub-urban scenario, where one side is blocked, and Figure 5d shows the severe urban environment, considering the high buildings, trees and traffic disturbances. It can be established that these typical environments have frequent variations. The list and details of all experiments are given in Table 2.  
In all the experiments, data are logged by the Septentrio Polarx55 receiver connected to PolaNt Choke Ring B3/E6 Antenna, and then post-processing is performed in MATLAB R2020a.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Experiment** & **Observation Sites** & **Constellation** & **Observation Period** \\ \hline \multirow{2}{*}{Exp1} & Clear Open Sky & GNSS & 06:00 a.m. to 12:00 p.m. \\  & (Static) & (G + R + C + E) & 2021-01-25 \\ \hline \multirow{2}{*}{Exp2} & Mediocre Multipath & GNSS & 06:00 a.m. to 12:00 p.m. \\  & (Static) & (G + R + C + E) & 2021-01-26 \\ \hline \multirow{2}{*}{Exp3} & Worst Multipath & GNSS & 06:00 a.m. to 12:00 p.m. \\  & (Static) & (G + R + C + E) & 2021-01-27 \\ \hline \multirow{2}{*}{Exp4} & Urban Canyon & GNSS & 06:00 a.m. to 12:00 p.m. \\  & (Moving) & (G + R + C + E) & 2021-01-28 \\ \hline \end{tabular} Notes: G = GPS, R = GLONASS, C = BeilDou, E = Galileo.  
\end{table}
Table 2: Observation sites, Observation periods and Constellation for experiments performed  
Figure 4: Candidate sites for Static experiments; (**a**) Best case environment, (**b**) Mediocre Multipath, (**c**) Worst Multipath (highlighted in box).