## 5 Experimental Results
### Discussion and Performance Analysis  
From Figure 11, it can be noted that using TDOA to geolocate the UAV as an RFI emitter is problematic since the TDOA is based on the difference in arrival time. In contrast, FDOA estimation is based on frequency Doppler contrast. In this way, it is possible to observe how the LEO satellite high-accelerated linear velocity affects the rate of FDOA calculations when using FDOA alone or in combination with TDOA/FDOA, which is crucial for identifying and following the UAV. As a result, the RMSE convergence speed when using FDOA individually, or a combination of TDOA/FDOA is faster than when using only TDOA in identical situations. Figure 12 shows the particulars of the peak and cross-correlation of TDOA and FDOA in the CAF-Map. The distance deviation, in this case, was only 650 m, and there were no issues with left-right ambiguity. The CAF-resolution maps were set to 500 m, so the results were extremely encouraging. The implications of slight adjustments in the geometry of the sensing platforms were demonstrated in this scenario. Figure 11: RMSE for TDOA, FDOA, and TDOA/FDOA. From Figures 13-15, it can be noted that the robust UAV (as an RFI emitter) tracking occurred when applying high-degree nonlinear tracking filtering. From Figure 14, we can note a high diversion of 3 rd-degree CKF tracking because of the sensitivity of CKF. From Figures 16-19 the RMSE convergence speed when using FDOA individually or a combination of TDOA/FDOA is faster than when using only TDOA in identical situations. As a result, we can note changes of a high-degree nonlinear filtering tracking based on TDOA/FDOA, where the 3 rd-degree CKF displays much larger errors than the 3 rd-degree GHQF or 5 th-degree GHQF 3 rd-degree GHQF maintains a good performance, but it is less accurate than the 5 th-degree GHQF, which has the best performance and an indistinguishable accuracy. In Figures 17b and 18c, we can note the evidence that CKF is very sensitive to initialization values compared to GHQF. The GHQF is more accurate and has faster convergence to the real state. CKF requires more time to converge and propagate higher error during the first 5 s. Some solutions exist using adaptive forms of CKF or  
Figure 12: Snapshot of CAF peak and cross-correlation for RFI emitter location estimation. (**a**) CAF peak of RFI emitter estimation. (**b**) Cross-correlation of TDOA and FDOA. what we call stochastic CKF sampling, in order to perform better initialization robustness. However, that is another problem to solve and will be considered in future works. Figure 14: Zoom-in of Figure 13—From the result, one can note that high-nonlinearity filtering of 5 th-degree GHQF and 3 rd-degree GHQF are more reliable tracking compared to 3 rd-degree CKF. Figure 13: Experimental RFI emitter-tracking based on TDOA/FDOA measurements: Results of CKF 3 rd degree, GHQF 3 rd degree, and GHQF 5 th degree compared with the true trajectory. Figure 15: Experimental RFI emitter-tracking based on TDOA/FDOA measurements: Results of CKF, 5 th degree HCKF-V1, 5 th degree HCKF-V2, 5 th degree HCKF-V3, 7 th degree HCKF-GENZ, and GHKF 3 rd degree compared with the true trajectory. Figure 16: RMSE for GHQF approach based on TDOA: The figure concludes the performance results of GHQF 5 th compared to GHQF 3 rd, and CKF 3 rd based on TDOA produced a high divergence of RMSE and difficulty in tracking the UAV as RFI emitter when using TDOA measurement. (**a**) RMSE of position. (**b**) RMSE of velocity. (**c**) RMSE of rotation. Figure 17: RMSE of GHQF approach based on FDOA: The performance result of GHQF 3 rd-degree and 5 th-degree approach compared to the CKF 3 rd degree based on FDOA is better than the approach based on TDOA. (**a**) RMSE of position. (**b**) RMSE of velocity. (**c**) RMSE of rotation. Figure 18: RMSE of GHQF approach based on TDOA/FDOA: In this figure, the lower error bound was achieved by GHQF 3 rd and GHQF 5 th.