## 4 Simulation Experiments and Results
### Simulation Experiments and Results
These figures show the weights of the beamforming pattern at various angles, and the red dashed line represents  
\[RMSE_{DOA}=\frac{1}{K}\sum\limits_{k=1}^{K}\sqrt{\frac{1}{N_{MC}\cdot N_{a}} \sum\limits_{l=1}^{N_{MC}}\sum\limits_{i=1}^{N_{a}}\Bigl{[}\left(\theta_{i}^{k }-\hat{\theta}_{i}^{k}\right)^{2}+\left(\varphi_{i}^{k}-\hat{\varphi}_{i}^{k} \right)^{2}\Bigr{]}}. \tag{49}\]8 RMSE of location estimation for multiple RFI sources: (**a**) RMSE of geolocation estimation for Case 1; (**b**) RMSE of geolocation estimation for Case 2 and 3.  
the direction of interference, and the black dashed line represents the direction of the connection between the center of the scene and the array. As shown in Equation (51), the signal in the interference direction will be weakened by multiplying these weights with the signal matrix, which achieves interference suppression. Next, the BP imaging algorithm is applied to the signal after interference suppression to get the image results in \[RMSE_{DOA}=\frac{1}{K}\sum\limits_{k=1}^{K}\sqrt{\frac{1}{N_{MC}\cdot N_{a}} \sum\limits_{l=1}^{N_{MC}}\sum\limits_{i=1}^{N_{a}}\Bigl{[}\left(\theta_{i}^{k }-\hat{\theta}_{i}^{k}\right)^{2}+\left(\varphi_{i}^{k}-\hat{\varphi}_{i}^{k} \right)^{2}\Bigr{]}}. \tag{49}\]9 Figure 17a shows the SAR image of the RFI-free echo signal. Then, the BP imaging algorithm is used to process the echo signals in Case 2 that are interfered by three RFI sources of the same type to obtain contaminated SAR images in Figure 17b,d,f. They are interfered by RFNI, NBLFMI, and WBLFMI, respectively. Figure 17c,e,g represents the imaging results after interference suppression on the signals in (b), (d), and (f). Likewise, (h) and (i) are the images for Case 3. It can be seen from the interfered images that when subjected to different types of RFI, the interference in the image is different. For the images after interference suppression, RFI is greatly suppressed, but the quality of these images is not as good as the RFI-free images. Therefore, the ISR of the signal before and after interference suppression can be used to show the degree of RFI suppression. The ISRs before and after interference suppression are calculated after obtaining the RFI suppressed signal. The ISR before interference suppression \(ISR_{bef}\) is obtained by Equation (18), the ISR after interference suppression \(ISR_{aft}\) and the improvement of ISR of the signal \(ISR_{imp}\) can be written as:  
Figure 7:0 Figure 7:1  
Figure 7:2 Beamforming pattern weights calculated by using the elevation and azimuth estimation results at half of the azimuth time for Cases 1–3: (**a**) and (**b**) are the result of the RFI type being RFNI in Case 1; (**c**) and (**d**) are the result of the RFI type being NBLFMI in Case 2; (**e**) and (**f**) are the result of Case 3. Figure 7:3 _Cont._  
Table 3 shows the improvement of ISR after applying the above interference suppression methods to the signals in Figures 8, 10 and 12, respectively. For Case 1, because of the higher location accuracy of the RFI source, the improvement of the signal ISR is better than other situations. In Case 2, the location accuracy is the worst for the interference type WBLFMI, which also leads to the smallest improvement in its ISR. The improvement of Case 3 is better than that of WBLFMI in Case 2, which is also consistent with the result of location accuracy.