## 4 Simulation Results and Analysis
As shown in Figure 9, it can be seen that at the 0\({}^{\circ}\) position, the error between the RCS maximum value of the optimal result of electromagnetic characteristic fitting and the corresponding position of the fitted electromagnetic characteristic is 0.29 dBsm, which meets the fitting requirements. At the same time, the second highest RCS peaks appeared at \(-7^{\circ}\) and \(7^{\circ}\), with values of 7.563 dBsm, respectively, again within 1 dB of the simulated target meeting the corresponding fitting requirements. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Number of Iterations** & \(a\) & \(b\) & \(c\) & \(d_{1}\) & \(d_{2}\) & \(F\) \\ \hline
1 & 000 & 000 & 111 & 000 & 000 & 3.3812 \\
2 & 001 & 111 & 111 & 000 & 000 & 2.9242 \\
3 & 100 & 111 & 100 & 011 & 001 & 2.0133 \\... &... &... &... &... &... &... \\
45 & 011 & 011 & 011 & 011 & 1.1456 \\
46 & 100 & 100 & 010 & 101 & 000 & 1.0425 \\
47 & 011 & 011 & 011 & 101 & 000 & 0.9742 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Electromagnetic characteristic parameter fitting optimization results. Figure 8: Objective function optimization results. Then, the combined capsule network and genetic algorithm proposed in this paper were utilized to simulate the RCS distribution characteristics of typical aircraft in the horizontal dimension from \(-180^{\circ}\)-\(180^{\circ}\) in Reference [24]. The fitting results obtained were compared with the typical data of the aircraft, as shown in Figure 10.  
Then, the error evaluation indicators were used to conduct a comparative analysis of the results. The error calculation results corresponding to the peak points are shown in Table 6. It can be seen that the RCS errors at the vast majority of peak points meet the requirement that the error does not exceed 1 dB. However, the RCS errors at the positions of \(-150^{\circ}\) and \(150^{\circ}\) exceed 1 dB, which are 1.69 dBsm and 1.63 dBsm, respectively. This is due to the existence of relatively strong scattering points in the \(\pm 30^{\circ}\) positions at the tail of the aircraft caused by the reflections of the horizontal and vertical tail fins. Meanwhile, the combined body can only form a strong scattering area through the mutual reflections between the corner reflectors, and thus it is unable to fully simulate the RCS peak value at this position. Overall, the algorithm adopted in this paper can simulate the typical data of the aircraft with relatively high accuracy. Figure 10: Compared results of the fitting results obtained using the proposed algorithm and the aircraft typical data. Figure 9: The RCS distribution characteristics obtained by the algorithm in this paper are compared with the typical aircraft data. Finally, the CapsNet + GA algorithm was utilized to train the typical data of other targets, such as ships and armored vehicles [25], and the corresponding fitting results of the RCS distribution characteristics were obtained, as shown in Figure 11.  
It can be seen from the comparison results in Figure 11 that, for the ship target and armored vehicle, both the RCS peaks and the average RCS values in the non-peak areas have a relatively good simulation effect. The RCS errors all meet the error index requirements, indicating that the CapsNet + GA algorithm proposed in this paper is applicable to the accurate simulation of the RCS distribution characteristics of other different targets.