## 2 Materials and Methods
### Dataset Generation and Problem Description
#### 2.3.2 Problem Description  
This method in Section 3.1 constructs a C/N\({}_{0}\) distribution map by utilizing the similarity between GNSS receiver return information and image grayscale information. Once the C/N\({}_{0}\) distribution map is obtained, object detection methods from image processing can be employed to detect and locate GNSS interference sources as the targets of interest. Traditional object detection algorithms typically achieve target detection by extracting image features and combining them with classification methods such as Support Vector Machine (SVM). However, these methods are computationally intensive and suffer from poor generalization performance. With the rapid development of deep learning, deep learning-based object detection methods have been widely adopted, offering advantages in accuracy, efficiency, and automation compared to traditional methods. In scenarios such as the coexistence of multiple directional interferences, increased diversity and density of GNSS interference, and the presence of multiple low-power interference sources, further  
Figure 3: Example of C/N\({}_{0}\) distribution maps from the dataset.  
solutions are needed to address the following issues in target localization using the C/N0 distribution map:  
(1) The estimation of the main lobe direction of the directional interference source antenna.  
Figure 4 presents illustrative examples of targets in the C/N0 distribution map under different pointing angles of directional interference using a Horn antenna. As shown, when the interference source uses a directional antenna, its radiation pattern is non-uniform and non-omnidirectional, with the main lobe exhibiting significantly higher gain than other directions. As a result, a shadow region with reduced C/N0 appears in the direction of the antenna's main lobe. To accurately localize directional interference sources, it is necessary to identify the main lobe direction of the directional antenna. Therefore, the target detection algorithm used must be capable of recognizing rotated targets and outputting their rotation angles.  
(2) The problem of multi-type antenna target feature recognition and classification.  
Figure 5 presents a schematic diagram of different types of interference source antennas at an equivalent radiated power of 0 dBm in the C/N0 distribution map. As illustrated, interference sources with different antenna types exhibit distinct target characteristics. Therefore, to enable the effective localization of multiple GNSS interference sources under various scenarios, it is essential to first learn the features of each target type and then classify them accordingly.  
Figure 4: Examples of targets under different antenna pointing angles of Horn antenna interference.  
Figure 5: Examples of targets from interference sources with different antenna types.  
(3) The problem of detecting and localizing weak GNSS interference source targets in the distribution map.  
Figure 6 illustrates targets of interference sources with different antenna types at an equivalent radiated power of \(-15\) dBm in the C/N\({}_{0}\) distribution map. As shown, weak GNSS interference sources typically affect only a limited number of navigation receivers, resulting in small-sized targets within the distribution map. To improve the accuracy of detection and localization for such weak interference sources, it is essential to enhance the deep learning model's ability to learn and distinguish the features of small targets associated with various types of weak GNSS interference.