## 3 Results and Discussion
Experiment 3: Simulation of Detection and Localization Performance Under Varying Interference Transmission Powers  
This section evaluates the performance of the proposed method in detecting and localizing multiple interference sources using the optimized ResNet-50 network under varying transmission power levels. 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 was set to three 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 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) For each interference type setting, the interference source power was set to 0 dBm, \(-5\) dBm, and \(-15\) dBm, respectively. (5) For each combination of interference type and power, the localization performance of the method was tested with varying numbers of interference sources. The number of interference sources was set sequentially from 2 to 16, with 100 Monte Carlo trials conducted for each case. In each trial, the numbers of omnidirectional and directional interference sources were assigned randomly. (6) 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 18, where Figure 18 presents the statistical results for \(acc_{ce}\), while Figure 18b presents the statistical results for \(RMSE_{M}\). The result curves under different interference source power levels and interference source type settings were plotted. For instance, 0 dBm_NoT_3 represents the curve where the number of interference source types is three and the interference source power is 0 dBm. The results demonstrate that the proposed method effectively adapts to complex scenarios with weak interference sources. However, as the number of interference types increases and the interference power decreases, the detection and localization performance of the proposed method exhibits a slight decline. As the interference transmission power decreases, changes in transmission power have a greater impact on the localization performance of the proposed method than the increase in the number of interference types. In this experiment, under the condition of an interference power of \(-15\) dBm, interference type six, and 18 interference sources, the proposed method achieves an interference detection rate \(acc_{ce}\) of 60% and a positioning accuracy \(RMSE_{M}\) of 1.8 km. Figure 19 illustrates the interference detection and localization performance under different interference transmission powers when the interference type is set to six. Figure 19a-c show the detection and localization performance of the proposed method when the interference transmission power is 0 dBm, \(-5\) dBm, and \(-15\) dBm, respectively. The method can effectively detect and accurately locate multiple types of weak interference. In a challenging scenario where the interference type is set to six, the interference transmission power is \(-15\) dBm, and the number of interference sources is as high as 18, the proposed method can still achieve a detection recognition rate of over 60% and an interference localization accuracy within 1.8 km. Figure 18: Simulation results under varying interference transmission powers. (**a**) Statistical results of \(acc_{cor}\). (**b**) Statistical results of \(RMSE_{M}\).