# Research on Optimization of Target Positioning Error Based on Unmanned Aerial Vehicle Platform
## 5 Simulation Experiment
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Parameters** & **DBO** & **CCDBO** & **GWO** & **PSO** & **SSA** \\ \hline \(\sigma_{x}\) & 10.4978 & 9.8242 & 10.0492 & 10.1540 & 10.5347 \\ \(\sigma_{y}\) & 7.89424 & 7.71265 & 7.73597 & 7.73624 & 7.90042 \\ \(\sigma_{z}\) & 7.24159 & 7.19744 & 7.21812 & 7.21954 & 7.25114 \\ \hline \end{tabular}
\end{table} 
Table 2: Optimal parameters. The root mean square error is an indicator of the algorithm's localization performance and is calculated as shown in Equation (29). \[RMSE=\sqrt{\frac{1}{n}{\sum\limits_{i=1}^{n}{\left({X-X_{i}}\right)^{2}}}} \tag{29}\]  
where \(n\) represent the number of target information, \(X\) denotes the real value of the target information, and \(X_{i}\) stands for the simulated value of the target information. For the above processing methods, the root mean square error is calculated for the latitude and longitude of the target point, respectively, and the results are shown in Table 4. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Parameters** & **Symbol** & **Standard Deviation** & **Value** \\ \hline UAV Latitude & \(B/^{\circ}\) & 0.0001 & 30\({}^{\circ}\) \\ UAV Longitude & \(L/^{\circ}\) & 0.0001 & 100\({}^{\circ}\) \\ UAV Height & \(H/m\) & 5 & 1100 m \\ UAV Yav Angle & \(\Phi/^{\circ}\) & 0.2 & 45\({}^{\circ}\) \\ UAV Pitch Angle & \(\theta/^{\circ}\) & 0.2 & 0 \\ UAV Roll Angle & \(\sigma/^{\circ}\) & 0.2 & 30\({}^{\circ}\) \\ Pod Heading Angle & \(\alpha/mrad\) & 2 & 45\({}^{\circ}\) \\ Pod Pitch Angle & \(\beta/mrad\) & 2 & 45\({}^{\circ}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Simulation experiment data. Figure 6: Scatterplot distribution of simulated latitude and longitude of target points: (**a**) Unprocessed data. (**b**) Data processed according to the equal influence principle. (**c**) Data processed by the original dung beetle algorithm. (**d**) Data processed by the improved dung beetle algorithm. Figure 7: Distribution of target point simulation latitude and longitude histograms: (**a**) Unprocessed data. (**b**) Data processed according to the equal influence principle. (**c**) Data processed by the original dung beetle algorithm. (**d**) Data processed by the improved dung beetle algorithm. Figures 6 and 7 show that the processed simulation point is closer to the true value point, and Figure 8 visualizes the distance between the simulation and true value points, while Table 4 reflects that the proposed algorithm has the smallest root mean square error, which proves that the CCDBO algorithm can effectively improve the accuracy of the target localization results. Compared with the other results, the simulation point processed by the CCDBO algorithm is more accurate, respectively, by 38.97%, 23.48%, and 20.42%. The error distances from the true value point were calculated for the three sets of data, as shown in Table 7.  
Taking target point 1 as an example, the localization results of the ten measurement data sets are shown in Figure 10. Taking the due north direction as 0\({}^{\circ}\), the error distances of the target point processed in different ways are shown as polar coordinates. The comparison shows that the data error after CCDBO processing is smaller than those of the other processing methods. Figure 11a shows the latitude and longitude of the target point with the true value after averaging the ten measurements. Figure 11b shows the straight-line distance between the average and target points; after processing, the data show significant error reduction compared with that of the unprocessed data. The percentage of reduction is shown in Table 8, indicating that the CCDBO algorithm can reduce the error of target localization.