## 5 Experimental Results
### Discussion and Performance Analysis
It appears the RMSE convergence speed is perfect when using GHQF 5 th degree based on hybrid TDOA/FDOA compared to conventional filters. (**a**) RMSE of position. (**b**) RMSE of velocity. (**c**) RMSE of rotation. Higher degrees of CKF and higher degrees of GHKF were implemented and it is shown that GHKF 5 th degree presented the best results in the three state estimation problems even with loss of observability of the third state. Table 2 presented the performance validation for the proposed approach implemented by extracted data from the Orolia Simulator and compared it to the theoretical simulation. Table 3 presents quadrature points and average execution time for 100 runs of the Monte Carlo iteration at the experimental task and theoretical simulation of the proposed filters. The main configurations of the computer used in this process are listed as the following: Intel (R) Core (TM) i7-6500 CPU @ 2.59 GHz; 8.00 GB RAM. According to the experimental task and theoretical simulation, the execution time of the filters is approximately proportional to the quadrature points. It is clear and easy to note that the GHQF approach based on Orolia modeling and TDOA/FDOA measurements for tracking an RFI emitter at high speed and a powerful rotation has achieved perfect tracking compared to conventional filters. It is crucial to compare the performance of the modeling and proposed approaches with the latest and \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & \multicolumn{3}{c}{**RMSEs of Algorithm**} & \multicolumn{3}{c}{**RMSEs of Algorithm**} \\  & \multicolumn{3}{c}{**Validation with the**} & \multicolumn{3}{c}{**Validation with the**} \\  & \multicolumn{3}{c}{**Experimental Task**} & \multicolumn{3}{c}{**Theoretical Simulation**} \\  & **Pos. (m)** & **Vel. (m/s)** & \(\mathbf{\omega}\) ( deg/s)** & **Pos. (m)** & **Vel. (m/s)** & \(\mathbf{\omega}\) ( deg/s)** \\ \hline
3 rd Deg CKF & 72.2575 & 43.8975 & 5.7510 & 69.1524 & 40.8675 & 4.2712 \\
3 rd Deg GHQF & 60.8740 & 38.8890 & 5.2535 & 58.9539 & 36.9493 & 4.2130 \\
5 th Deg GHQF & 60.0550 & 38.2520 & 5.1500 & 58.6646 & 36.5513 & 4.0128 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average RMSE for a position (Pos. ), velocity (Vel. ), and rotation rate (\(\omega\)) implemented by experimental and theoretical simulation. Figure 19: RMSE of GHIF 5 th- and 3 rd-degree approach based on TDOA/FDOA: In this figure, the lower error bound was achieved by GHQF 5 th. It appears the RMSE convergence speed is perfect when using GHQF 5 th degree based on hybrid TDOA/FDOA compared to conventional filters. recently developed methods [3]. For that, we have created various scenarios for muti-LEO satellites and a UAV as emitter using the Orolia Skydel simulator and implemented at CAF and GHKF 3 rd and 5 th degrees based on TDOA/FDOA. In our approach, the results have been presented in terms of position, speed, and rotational force (Omega). Although [3] created their scenario for one Geosynchronous Equatorial Orbit (GEO) satellite with three antennas and stationary Earth station as emitter using STK, they implemented their scenario to localize the emitter position employing the RSS method.