## 2 Materials and Methods
### Dataset Generation and Problem Description
#### 2.3.1 Dataset Generation
```
INPUT: The number of receivers \(M\) INPUT: Monitoring area width length \(L\) and width \(W\) INPUT: The receiver returned data set \(\{X_{1},X_{2},\ldots,X_{M}\}\) INPUT: The C/N\({}_{0}\) threshold \((C/N_{0})_{CT}\) INPUT: The C/N\({}_{0}\) value under interference-free \((C/N_{0})_{\max}\) INPUT: Set the distance threshold \(Dp\) for selecting C/N\({}_{0}\) data when assigning grayscale values to pixels OUTPUT: The C/N\({}_{0}\) distribution map \(ICN\) 1: Calculate receiver density as \(Rd=M/L\times W\) 2: Set the localization accuracy as \(Lp=1/\left\lceil Rd/40\right\rceil\times 2\) 3: Calculate the width of the C/N\({}_{0}\) distribution map as \(W_{ICN}=L/Lp\), the height of the C/N\({}_{0}\) distribution map as \(H_{ICN}=W/Lp\), and the number of pixels as \(N=W_{ICN}\times H_{ICN}\) 4: Initialize the gray values of all pixels in the C/N\({}_{0}\) distribution map \(ICN\) to 0 corresponding to the C/N\({}_{0}\) under interference-free conditions 5: While \(l\leq N\) do 6: Obtain the receiver returned data set \(\left\{X_{l,D1},X_{l,D2},\ldots,X_{l,DM}\right\}\) that are within a distance of \(Dp\) from the pixel 7: Calculate the weighted average \((C/N_{0})_{IM}\) of the C/N\({}_{0}\) data for the \(DM\) data points, where the weights are the inverse of the distance to the pixel 8: If \((C/N_{0})_{IM}>(C/N_{0})_{CT}\) then 9: Calculate the grayscale value \(Gray_{l}\) corresponding to the value \((C/N_{0})_{IM}\) using Formula (8) and write it to the corresponding pixel 10: Else 11: Write the grayscale value 255 to the corresponding pixel 12: End if 13: End while 14: Perform a dilation operation on \(ICN\) 15: Perform a median filtering operation on \(ICN\) 16: Return the C/N\({}_{0}\) distribution map \(ICN\)
```  
**Algorithm 1**Generation method of the CNR distribution map  
(5) Based on the system model in Section 2, the navigation signal C/N\({}_{0}\) for each receiver in multiple interference source scenarios can be calculated for each experiment. On this basis, the method in Section 3.1 can be used to generate a sample of the C/N\({}_{0}\) distribution map for this experiment. The dataset generated through the aforementioned steps was utilized for training and testing the proposed target detection method. Representative examples of C/N\({}_{0}\) distribution maps from the dataset are illustrated in Figure 3. As shown, interference effects cause signal C/N\({}_{0}\) degradation, forming interference source shadow targets. Consequently, the interference source localization problem can be transformed into a target detection task within the C/N\({}_{0}\) distribution maps. In these maps, omnidirectional and directional interference sources exhibit distinct image characteristics under different parameter configurations. Figure 2: Antenna radiation patterns for different antenna models. For the scenario addressed in this study, the objectives were to identify target types and precisely localize interference sources within the C/N\({}_{0}\) distribution maps. Deep learning-based target detection methods offer significant advantages in terms of both accuracy and computational efficiency. Therefore, this work employed a deep learning-based detection framework to achieve robust interference source detection and localization.