## 3 Results and Discussion
Experiment 2: Simulation of Detection and Localization Performance for Multi-Type Interference Sources  
This section primarily evaluates the effectiveness of the proposed method in detecting and localizing multiple interference sources under multi-type interference scenarios using the optimized ResNet-50 network. 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) The number of interference types as set between two and six, with the typical omnidirectional antenna being a dipole antenna. Typical directional antennas include Yagi-Uda, horn, Vivaldi, helix, and patch microstrip 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) The localization performance of the proposed method was tested under each interference type configuration with varying numbers of interference sources. The number of interference sources was set sequentially from 2 to 16, with 100 Monte Carlo trials  
Figure 15: Example of increased localization error. (**a**) The interference target is located at the edge of the image. (**b**) Aliasing occurs due to the close spacing of interference sources. conducted for each case. In each trial, the number of omnidirectional and directional interference sources was assigned randomly. (5) The detection and recognition accuracy of interference sources, \(acc_{ce}\), was calculated, along with the localization error, \(RMSE_{M}\), when the interference was correctly detected and recognized. The statistical methods are consistent with those described in Section 3.1. The experimental results are shown in Figure 16, where Figure 16 presents the statistical results for \(acc_{ce}\), while Figure 16 presents the statistical results for \(RMSE_{M}\). The curves labeled NoT_2 to NoT_6 correspond to the cases where the number of interference types is set from two to six, respectively. The results demonstrate that the proposed method effectively adapts to complex scenarios with multiple interference types. However, as the number of interference types increases, both detection and localization performance exhibit slight degradation. In this experiment, under the condition of interference type 6 and 18 interference sources, the proposed method achieves an interference detection rate \(acc_{ce}\) of 70% and a positioning accuracy \(RMSE_{M}\) of 1.05 km. Figure 17 illustrates examples of detection and localization performance for multi-type interference. Figure 17a-c show the interference detection and localization results in complex scenarios with interference types set to four, five, and six, respectively. It can be seen that the method proposed in this paper effectively achieves accurate detection and high-precision localization for multiple types of interference. In scenarios where the interference type is set  
Figure 16: Simulation results for multi-type interference detection and localization. (**a**) Statistical results of \(acc_{ce}\). (**b**) Statistical results of \(RMSE_{M}\). to six and the number of interference sources reaches up to 18, this method can still achieve a detection recognition rate of over 70% and a localization accuracy within 1 km. Figure 17: Examples of multi-type interference detection and localization performance. (**a**) Detection and localization performance with four interference types. (**b**) Detection and localization performance with five interference types. (**c**) Detection and localization performance with six interference types.