## 4 Numerical and Anechoic Chamber Experiments  
This section begins with an overview of the datasets and the specifics of the training process. Subsequently, a comparative evaluation of the detection performance across various networks is presented. Following this, the results of ablation studies focused on quantitative assessments and the impact of network components are discussed. Furthermore, experiments conducted in anechoic chambers are performed to demonstrate the superiority of robustness when compared to other networks. A. Dataset construction and experimental details  
The method is verified by electromagnetic calculation data and an anechoic chamber. The simulation data are constructed and supported by the electromagnetic simulation software and satellite CAD model. The imaging parameters are configured to the following specifications: the center frequency is 220 GHz, the bandwidth is 20 GHz, the number of sampling points is 1200; the azimuth aperture is 3\({}^{\circ}\) with 500 points for sampling. The depression angle is set as 45\({}^{\circ}\). Taking into account the imaging resemblance between neighboring observation apertures, a set of 617 original images is acquired by selectingthem at a 0.58\({}^{\circ}\) interval from a total of 360\({}^{\circ}\). Figure 9 demonstrates various representative imaging outcomes across a range of observation apertures. Next, we need to label the target for detection based on CPICs. Provided that the primary components of the satellite and parabolic antenna are discernible in the image, they are marked or labeled. Furthermore, the difference in the bounding boxes of the satellite and the antenna is illustrated in Figure 10. We can see that the bounding boxes of the satellite are significantly larger than the three kinds of bounding boxes for the parabolic antenna, particularly the bounding box for the specular point, which tests the multi-scale detection capability of the object detection network. Additionally, the distribution of the three bounding boxes for the parabolic antenna is uneven. Among them, there are approximately 400 types of ellipse arc, 100 types of edge pair-points, and around 50 types of specular point. The total number of ellipse arc and edge pair-points accounts for nearly 90%, and together they form the main part of the antenna. Figure 10: The distribution of bounding boxes within the dataset. Figure 9: The training samples under different apertures. The mean Average Precision (mAP) serves as a crucial measurement for assessing the performance of deep learning models. It is of paramount importance due to its ability to provide a comprehensive evaluation of accuracy and recall capabilities across various trained instances. The choice of mAP as an evaluation criterion is justified because it is extensively utilized in numerous object detection research studies, offering a standardized and reliable way to compare the effectiveness of different models in identifying and recalling objects accurately. We choose mAP50 and mAP50-95 to evaluate the training results. The mAP50 metric measures the average precision of the model when the IoU (Intersection over Union) threshold is set to 0.5, while mAP50-95 measures the average precision of the model over the IoU threshold range from 0.5 to 0.95. Figure 11 shows the changes in mAP during the training process for different networks. The left side of Figure 11 shows that the training results of YOLOv3 are poor, and the training was terminated before reaching 0.7; YOLOv8 has an mAP50 around 0.8, which is a little better training effect than in YOLOv5 and YOLOv6. The proposed methods both have mAP50 values exceeding 0.9, maintaining a high level of training. The right side of Figure 11 gives the comparison result of mAP50-95. It shows that apart from the overall numbers having decreased by about 0.3, the overall trend remains unchanged.