# Intelligent Detection Method for Satellite TT&C Signals under Restricted Conditions Based on TATR
## 6 Experiment and Results
### Experimental Results and Analysis
#### 6.3.3 Comparative Experiment  
Additionally, to validate the superiority of the proposed method, we compared the detection performance of the TATR with four traditional object detection methods, including Faster-RCNN [23], YOLOv5 [26], YOLOv7 [28], and DETR [37]. The quantitative comparison of performance indicators is presented in Table 4.  
According to Table 4, the signal detection performance of the TATR model excels, achieving the best results. YOLOv5s, YOLOv5m, and YOLOv5l represent different versions of the YOLOv5 model, while similarly, YOLOv7, YOLOv7w, and YOLOv7x denote varying versions of the YOLOv7 model, with an increasing number of parameters leading to an overall enhancement in detection accuracy. In comparison to the classical Faster-RCNN (R50) model, the TATR-6 layer model exhibits remarkable improvements of 14.03% and 17.74% in the [EMAIL_ADDRESS] and [EMAIL_ADDRESS]:0.95 metrics, respectively. Furthermore, compared to DETR, TATR shows a 13.46% enhancement in [EMAIL_ADDRESS]:0.95, a 12.98% improvement over the YOLOv5l model, and a 6.59% boost over the YOLOv7x model. The significant performance enhancement primarily manifests in the [EMAIL_ADDRESS]:0.95 metric, underscoring the superior precision of the proposed approach in detecting signals with the presence of accurate detections being crucial for signal parameter estimation, thereby substantiating the superiority of the algorithm proposed in this study for signal detection tasks. Figure 11: The feature visualization results under various SNRs of TATR: (**a–f**) show SNRs from 10 dB to \(-\)15 dB in 5 dB decrements, respectively. The red box indicates the true signal location, while the bright areas in the heat map show the key focus of the network after training. At high SNRs, it targets the signal peak, then the network shifts attention to sidelobes and the context envelope in low SNR conditions. It is noteworthy that in terms of the AR, TATR slightly underperforms compared to the YOLO series models. This discrepancy may stem from the inappropriate preset anchor points in the YOLO series models, leading to partial false alarms during detection. Notably, in the [EMAIL_ADDRESS] metric, TATR exhibits a 6.56% and 6.31% improvement over the larger-scale versions of YOLOv5l and YOLOv7x, respectively, indicating an increased intersection between predicted signal boxes and ground truth signal boxes, resulting in higher IoU values for the detected boxes. Regarding signal parameter estimation error, the TATR algorithm demonstrates a reduction in estimation error ranging from 2.5% to 0.3% in comparison to other algorithms, highlighting that the integration of global and local attention mechanisms in the proposed approach is more conducive to capturing signal spectral features and facilitating more precise signal detection. The outcomes of parameter estimation will further impact non-collaborative signal analysis, with enhanced parameter estimation accuracy contributing to the seamless integration of TATR into signal analysis systems. Additionally, the proposed model boasts a reasonable number of parameters, facilitating practical deployment of the model. To further validate the effectiveness of our proposed method, we visually compare the detection results of TATR with the models mentioned above. The results, as shown in Figure 12, clearly indicate that our method achieves the best detection performance across various SNRs compared to the ground truth. Other detection algorithms exhibit false positives or low IoU with detection boxes, which can be fatal for TT&C signal detection and parameter estimation tasks, as detection boxes that are either too large or too small can introduce errors in parameter estimation. Moreover, when similar background signals or interference signals appear around the TT&C signal, our network demonstrates more accurate recognition and localization. For example, in the results displayed at 0 dB and 10 dB, other models exhibit cases where the detection results include other signals.