## 3 Results and Discussion
Similarly, OSF-RCNN-VGG16 and OSF-RCNN-ResNet50 represent the simulation results of our method using VGG-16 and ResNet-50 architectures, respectively. The simulation results demonstrate that the CCC method exhibits significant degradation in interference detection accuracy as the number of directional interference sources increases, while ResNet-50 outperforms VGG-16 in both interference identification and quantity estimation. When using the ResNet-50 network, both the OSF-RCNN and Faster R-CNN methods can perform interference detection and estimate the number of interference sources, with OSF-RCNN achieving better performance. When different networks are used, the detection and recognition performance of Faster-RCNN-ResNet50 is significantly better than that of OSF-RCNN-VGG16, indicating that the choice of network has a considerable impact on the effectiveness of interference detection and recognition. OSF-RCNN-ResNet50 achieved the best interference detection and localization performance in this experimental scenario. In this experiment, when the number of interference sources reaches 18, the proposed method achieves an interference detection rate \(acc_{ce}\) of 77% and a positioning accuracy \(RMSE_{M}\) of 0.5 km. Figure 11: An example of a Monte Carlo trial. (**a**) The spatial distribution of the interference sources and receivers. (**b**) The corresponding C/N\({}_{0}\) distribution map. Figure 13 presents the experimental comparison between the OSF-RCNN-ResNet50 and OSF-RCNN-VGG16 methods. The OSF-RCNN-VGG16 method misidentifies the directional interference highlighted by the red circle as omnidirectional interference. Compared to the VGG-16 network, the ResNet-50 network has advantages such as a deeper architecture and better gradient propagation, making it more suitable for detecting and localizing directional interference in scenarios involving multiple interference sources. Figure 14 presents the experimental comparison between the OSF-RCNN-ResNet50 and Faster-RCNN-ResNet50 methods. Figure 14 compares the localized results of the horn directional interference (highlighted in the top-left corners of Figure 14,b with the ground-truth interference source position). It can be observed that the Faster-RCNN-ResNet50 method fails to accurately estimate the main lobe direction of the directional antenna, resulting in the deviation in the interference source position estimation. Figure 12: Simulation results for directional interference detection and localization. (**a**) Statistical results of \(acc_{ee}\). (**b**) Statistical results of \(RMSE_{M}\). As shown in Figure 15, with an increasing number of interference sources, phenomena such as target proximity to the edge of the C/N\({}_{0}\) distribution map and overlap between closely spaced targets lead to aliasing effects. In Figure 5a, where the number of interference sources is six, a localization error of up to 350 m occurs due to a target being near the edge of the C/N\({}_{0}\) map. In another case shown in Figure 15b, with 13 interference sources, localization errors of 538 m and 570 m are caused by overlapping targets. As the number of targets continues to increase, these issues become more pronounced, resulting in further degradation in the localization accuracy. Figure 14: Comparison of experimental results between OSF-RCNN-ResNet50 and Faster-RCNN-ResNet50 methods. (**a**) Localization performance of OSF-RCNN-ResNet50. (**b**) Localization performance of Faster-RCNN-ResNet50. (**c**) Localization performance comparison. Figure 13: Comparison of experimental results between OSF-RCNN-ResNet50 and OSF-RCNN-VGG16 methods. (**a**) Localization performance of OSF-RCNN-ResNet50. (**b**) Localization performance of OSF-RCNN-VGG16. In conclusion, the CCC method is not applicable for interference source localization in scenarios with directional interference. The OSF-RCNN-ResNet50 method, with ResNet-50 as its backbone network, demonstrates superior performance in interference detection and identification, as well as interference source localization.