## 4 Result Analysis and Discussion
### Random 60\({}^{\circ}\) Obstruction Simulations-One Single Large-Angle Obstruction
\begin{tabular}{c} **Proposed** \\ **Algorithm** \\ **(with Random Obstruction)** \\ \end{tabular}  & \begin{tabular}{c} **Quasi\_Optimal** \\ **Algorithm** \\ **(with Random Obstruction)** \\ \end{tabular}  & \ \begin{tabular}{c} **Recursive** \\ **Algorithm** \\ **(with Random Obstruction)** \\ \end{tabular}  & \begin{tabular}{c} **Proposed** \\ **Algorithm** \\ **(with Random Obstruction)** \\ \end{tabular}  \\ \hline
10 & 1.431 & 1855.158 & 1.279 & 1.882 & 782.354 & 1.308 \\
20 & 0.985 & 32.339 & 0.940 & 1.082 & 9. Figure 6 shows the significant increase in the average GDOP when using the Quasi-Optimal algorithm based on randomly 60\({}^{\circ}\)-azimuthal satellites occluded by obstructions. The daily average GDOP difference decreases as the number of satellites increases, which shows a clear performance impact. For example, the difference is around 0.08 with 60-80 satellites, around 0.10 with 20-50 satellites, and 0.45 with 10 satellites with fewer observations. This indicates that obstruction has a significant impact on the performance of current classical geometric satellite selection algorithms, which implies limitations in engineering applications. Figure 7 shows a comparison of the performance of the proposed algorithm in an occluded and an open field. If there are no obstructions, the proposed algorithm is effective  
Figure 5: Average GDOP per day of different satellite sizes constructed by each algorithm without obstruction. Figure 6: Average GDOP per day of different satellite sizes constructed by the Quasi-Optimal algorithm under random obstruction or without obstruction. for overcoming the limitation that some of the satellites cannot be observed due to environmental signal blocking. There is basically no big difference between the average daily GDOP of the two conditions, which shows that they have similar performance under unobstructed conditions. However, the average daily GDOP difference for different numbers of satellites varies: It is only 0.02 for most cases, except for the 10-satellite case where the difference is 0.03. However, there is a situation in which the daily average GDOP is higher in an unoccluded environment than in an occluded environment; this is probably due to the fact that the genetic algorithm used is not specially optimized for this problem, and the local convergence phenomenon affects the final results. As shown in Figure 8, the proposed algorithm improves the performance of GDOP in an occluded environment. The average daily GDOP difference is 0.57 with 10 satellites, 0.14 with 20 satellites, 0.13 with 30 satellites, around 0.09 with 40-60 satellites, and around 0.07 with 70-80 satellites. Based on these results, it can be concluded that the proposed algorithm can effectively improve the localization accuracy under a single large-angle obstruction. Figure 8: Average GDOP per day of different satellite sizes constructed by each algorithm under random obstruction. Figure 7: Average GDOP per day of different satellite sizes constructed by the proposed algorithm under random obstruction or without obstruction. Figure 9 shows the case of selecting 10 satellites after applying the algorithm with a 240\({}^{\circ}\) obstruction. The GDOP difference of both algorithms is lower than 0.3 after 12 h; this is due to the fact that multiple small-angle obstruction more easily take a uniform distribution with respect to a single large-angle obstruction, so the satellites are more likely to take a uniform distribution. The daily average GDOP difference when selecting 10 satellites is 0.386, which is lower than that of the case of a single large-angle occluded case; however, the proposed algorithm shows a higher stability in the case of multiple small-angle obstructions.