## 5 Discussion  
In Section 4, by setting the system and interference parameters, we simulate and get the SAR echo signals contaminated by the three types of RFI, whose time domain and frequency domain forms are shown in Figure 7. Furthermore, a series of experiments are set up to discuss the performance of the 2-D ESPRIT method and the PSO algorithm under different type RFI sources and ISR conditions. For the 2-D ESPRIT method, Figures 8, 10 and 12 show the DOA error between the estimation result and the true value at each azimuth time. It can be seen that the estimation error of \(\theta\) does not exceed \(\pm 1^{\circ}\), and the estimation error of \(\varphi\) does not exceed \(\pm 0.5^{\circ}\), and most of the estimation errors are small. This demonstrates that the method has a good performance in the DOA estimation of RFI sources. Comparing these three figures, we can further find that the performance of the method varies under different RFI types and ISR conditions, which can be more intuitively reflected in the RMSE results of the angle estimation. Figures 9, 11 and 13 show that with the gradual increase of ISR, the RMSE of the angle estimation in the three cases decrease, which illustrates that the proposed method can achieve high angle measurement accuracy when the ISR is large. It can be seen from Section 3 that the presence of noise and echo signals will make the calculation result of Equation (32) contain components that are not related to RFI, which will cause  
Figure 17: SAR images obtained by applied the imaging algorithm to the echo signals: (**a**) RFI-free image; (**b**,**d**,**f**) are contaminated images for Case 2; (**c**,**e**,**g**) are RFI suppressed images of (**b**,**d**,**f**); (**h**,**i**) are contaminated and RFI suppressed images for Case 3. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Case** & **Type of RFI** & \(\textit{ISR}_{\textit{imp}}\) (**dB**) \\ \hline \multirow{3}{*}{Case 1} & RFNI & 28.83 \\  & NBLFMI & 27.82 \\  & WBLFMI & 27.03 \\ \hline \multirow{3}{*}{Case 2} & RFNI & 25.66 \\  & NBLFMI & 23.65 \\ \cline{1-1}  & WBLFMI & 19.84 \\ \hline \multirow{2}{*}{Case 3} & Mixed RFI & 21.92 \\ \cline{1-1}  & & \\ \end{tabular}
\end{table} 
Table 3: The range and approach of randomly generating RFI signal parameters. errors in the azimuth and elevation angles estimated by Equations (34) and (38). And the error increases as the ISR becomes smaller, which is consistent with the above experimental results. Meanwhile, it can be seen in the three figures that the method has different angle measurement capabilities for the three types of interference. It has the highest measurement accuracy for RFNI, followed by NBLFMI and WBLFMI. Because when the bandwidth of the RFI gradually increases, the error between the wavelength used in Equations (34) and (38) and the true wavelength of RFI increases, which will also bring error in angle measurement. In addition, we can see from the comparison between Figures 9 and 11 that the optimal value of RMSE estimated for the angle of RFNI in Case 1 is 0.0199\({}^{\circ}\), while in Case 2, the value is 0.1523\({}^{\circ}\), which indicates that when the number of channels is the same, the DOA estimation accuracy of this method for a single RFI source is better than that of multiple RFI sources. For the method of using DOA estimation results and PSO algorithm to estimate RFI source position coordinates, experiments also show the effectiveness of it. Illustrated in Figure 14, the particle swarm is gradually optimized to the target solution point as the iteration continues. However, the value obtained after the convergence of the PSO algorithm is slightly different from the true value, which is caused by the DOA estimation error and the random search solution process. Figure 14 also shows that the method can calculate the coordinate estimation value of the RFI source in the three cases.