## 3 Results and Discussion  
This section presents a simulation-based analysis of the proposed method. Experiments 1, 2, and 3 evaluate its performance in directional interference scenarios, multi-type interference scenarios, and weak interference scenarios for the detection and localization of multiple GNSS interference sources, respectively. The results demonstrate that the proposed method can achieve the effective localization of GNSS interference sources under these challenging conditions. Experiment 1: Simulation of Detection and Localization Performance for Directional Interference Sources  
This section primarily evaluates the performance of the proposed method under directional interference scenarios with varying numbers of interference sources. The comparative methods include the approach from Reference [25] and the Faster R-CNN algorithm. Specifically, both Faster R-CNN and the proposed method were evaluated using two representative network architectures: ResNet-50 and VGG-16. ResNet-50 and VGG-16 are currently mainstream backbone networks for image recognition. Both achieve a good balance among accuracy, efficiency, and generalization ability. VGG-16 constructs a deep architecture by continuously stacking small convolutional kernels, featuring a simple and regular structure. ResNet-50 introduces a residual learning mechanism and employs skip connections to effectively alleviate the degradation problem in deep networks [39]. The experimental data were acquired following the methodology described in Section 3.1, with the scenario configuration detailed below:  
(1) The GNSS interference monitoring area was set to 60 \(\times\) 60 km. (2) Receiver density varied randomly between 400 and 600 units per square kilometer, with spatial positions assigned randomly. (3) Two types of interference sources were implemented: standard omnidirectional dipole antennas and directional horn antennas. The transmission power of each source was set to 0 dBm. The direction of each interference source was randomly assigned within the range from 1\({}^{\circ}\) to 360\({}^{\circ}\), with a resolution of 1\({}^{\circ}\). All sources emitted narrowband interference signals with a quality factor of Q = 1. (4) Localization performance was evaluated across varying interference quantities (2-16 sources). For each source count, 100 Monte Carlo trials were conducted with randomized combinations of omnidirectional and directional sources. (5) The detection accuracy rate \(acc_{ce}\) was calculated as \(acc_{ce}=T_{ce}/100\), where \(T_{ce}\) represents successful detections across 100 trials. Meanwhile, the localization error was also statistically measured when the interference was correctly detected. The localization error was quantified as the mean root mean square error (RMSE) computed across multiple interference localization trials, measured in kilometers. The statistic \(RMSE_{M}\) was calculated using formula \(RMSE_{M}=\sum_{k=1}^{T_{ce}}\sum_{i=1}^{N_{jmmmer}}RMSE_{i}\), where \(N_{jmmmer}\) represents the number of interferences. In each Monte Carlo trial, the locations of the interference sources, as well as the number and locations of the receivers, were randomly generated. An example of such a trial is illustrated in Figure 11. Figure 11a shows the spatial distribution of the interference sources and receivers, while Figure 11b presents the corresponding C/N\({}_{0}\) distribution map generated based on the configuration in Figure 11a. The experimental results are shown in Figure 12, where Figure 12a presents the statistical results for \(acc_{ce}\), while Figure 12b presents the statistical results for \(RMSE_{M}\). In the figures, CCC represents the method from Reference [25], while our proposed method is denoted as OSF-RCNN. The curves labeled Faster-RCNN-VGG16 and Faster-RCNN-ResNet50 correspond to the Faster R-CNN algorithm implemented with VGG-16 and ResNet-50 backbones, respectively.