# A Study on a Radio Source Location Estimation System Using High Altitude Platform Stations (HAPS)
## 3 Numerical Analysis and Results
### Location Estimation Simulation
#### 3.2.5 Results of Location Estimation Simulation
This is thought to be due to the fact that the 10 km environment has smaller multipath components compared to the 5 km case, such that angular estimation has higher accuracy. Table 8 shows the results of the estimation using UAVs at three locations 20 km away from the urban area. Both the estimated probability and localization accuracy are degraded in this case. It is thought to be due to the fact that the sensors are located too far away, such that the arrival waves after multiple reflections are too weak compared to the noise level. The CDF of localization errors for these different scenarios is summarized in Figure 18. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Beamformer** & **MUSIC** \\ \hline Mean error [m] & 180.2 & 179.5 \\ \hline CDF90\% [m] & 365.2 & 358.9 \\ \hline Detection percentage [\%] & 17.8 & \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Results with UAV 10 km away. Figure 18: CDF of errors using UAV at each distance. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Beamformer** & **MUSIC** \\ \hline Mean error [m] & 277.8 & 227.7 \\ \hline CDF90\% [m] & 607.7 & 472.6 \\ \hline Detection percentage [\%] & 16.4 & 15.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Results with UAV 20 km away. Deuras  
Finally, a model simulating the DEURAS system was created by installing the receiving antennas on the rooftop of buildings with good visibility in an urban area, as shown in Figure 19. Other system parameters were the same as those for UAVs. Table 9 shows the evaluation results of position estimation simulations using the Beamformer and MUSIC methods where DEURAS sensors are located, as shown in Figure 19. Compared to location estimation using UAVs, the estimation accuracy is still poorer, except for the fact that DEURAS can provide a higher estimated probability since the sensors are close to the sources. For a fair comparison, we conducted new simulations that the HAPS and UAVs were deployed at three points at a distance of 500 m from the center of the urban area (just right above the area similar to the case of the DEURAS system). Table 10 shows the evaluation results of the estimation using the HAPS at three locations 500 m away from the urban area. Similarly, Table 11 shows the results of the evaluation when estimating using UAVs at three locations 500 m away from the urban area. Figure 19: Receiving antenna layout assuming the DEURAS system. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Beamformer** & **MUSIC** \\ \hline Mean error [m] & 444.8 & 444.9 \\ \hline CDF90\% [m] & 615.1 & 614.9 \\ \hline Detection percentage [\%] & & 79.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Results with antenna placement assuming the DEURAS system. Figure 20 shows a summary of the CDF of localization errors of all scenarios. These results show that the HAPS shows the best performance in terms of both estimated probability and localization accuracy. It is owing to the fact that the HAPS has a higher LoS ratio to guarantee a higher estimated probability, and also direct waves are more likely to arrive at the HAPS rather than multiple reflection paths that increase the angular estimation accuracy.