## 4 Experimental Verification
### Simulation Experiment
#### 4.1.2 Simulation Experimental Result  
Simulation experiments are conducted under the above parameters and scenario setting, and the experimental result is as follows. Figures 4 and 5 show the results of sensor measurement systematic error and attitude systematic error estimation for UAVs A and B, respectively. As can be seen from Figures 4 and 5, the results of the algorithm all converge to the set systematic error values quickly. Before 10 s, the initial value deviates from the true value, resulting in a large jitter. But at around 10 s they all converge gradually and remain stable, indicating the effectiveness of the proposed algorithm. Table 3 shows the root mean square error of the sensor measurement estimation systematic error and attitude estimation systematic error for UAVs A and B, respectively. From Table 3, it can be seen that the abatement rates of the sensor measurement systematic error and attitude systematic error of UAVs A and B are basically above 90%. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Standard Deviation of**} & \multicolumn{3}{c}{**Standard Deviation of**} \\  & \multicolumn{3}{c}{**Systematic Error**} & \multicolumn{3}{c}{**Random Error**} \\ \hline Sensor error & \(\begin{pmatrix}20&m&0.15^{\circ}&0.1^{\circ}\\ \end{pmatrix}\) & \(\begin{pmatrix}5&m&0.01^{\circ}&0.01^{\circ}\\ \end{pmatrix}\) \\ Attitude error & \(\begin{pmatrix}0.01^{\circ}&0.01^{\circ}&0.01^{\circ}\\ \end{pmatrix}\) & \(\begin{pmatrix}0.001^{\circ}&0.001^{\circ}&0.001^{\circ}\\ \end{pmatrix}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Error characteristic of UAV A. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**Standard Deviation of**} & \multicolumn{3}{c}{**Standard Deviation of**} \\  & \multicolumn{3}{c}{**Systematic Error**} & \multicolumn{3}{c}{**Random Error**} \\ \hline Sensor error & \(\begin{pmatrix}30&m&0.12^{\circ}&0.1^{\circ}\\ \end{pmatrix}\) & \(\begin{pmatrix}5&m&0.01^{\circ}&0.01^{\circ}\\ \end{pmatrix}\) \\ Sensor error & \(\begin{pmatrix}30&m&0.12^{\circ}&0.1^{\circ}\\ \end{pmatrix}\) & \(\begin{pmatrix}5&m&0.01^{\circ}&0.01^{\circ}\\ \end{pmatrix}\) \\ Attitude error & \(\begin{pmatrix}0.01^{\circ}&0.01^{\circ}&0.01^{\circ}\\ \end{pmatrix}\) & \(\begin{pmatrix}0.001^{\circ}&0.001^{\circ}&0.001^{\circ}\\ \end{pmatrix}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Error characteristic of UAV B.  
Figure 4: _Cont._  
Figure 4: Systematic error estimation of UAV A. (**a**) Range systematic error estimation of UAV A; (**b**) azimuth systematic error estimation of UAV A; (**c**) elevation systematic error estimation of UAV A; (**d**) yaw systematic error estimation of UAV A; (**e**) pitch systematic error estimation of UAV A; (**f**) roll systematic error estimation of UAV A.  
Figure 5: _Cont._  
Figure 6 shows the effect of tracking the maritime target after spatial registration when the cooperative platform is moving, and Table 4 shows the root mean square error of the X and Y directions as well as position estimation for different tracking methods in scenario one. As shown in Figure 6 and Table 4, the corrected single-platform and multi-platform fusion tracking effects are better than that of the uncorrected single-platform and multi-platform fusion. Due to sensor systematic errors and attitude systematic errors, the uncorrected tracking trajectories all deviate from the true value of the target motion trajectory, while the corrected single-platform tracking effect has been improved compared with the uncorrected one. However, there are still some deviations due to the coordinate transformation error and error estimation error, and the tracking accuracy is further improved by the fusion of the unified coordinate system of the cooperative platform. Figure 6 shows that the  
Figure 5: Systematic error estimation of UAV B.