# Intelligent Detection Method for Satellite TT&C Signals under Restricted Conditions Based on TATR
## 7 Discussion  
In previous studies, traditional time-domain processing methods often experience performance degradation or even failure in the absence of phase information. Deep learning approaches for TT&C signal spectrum detection are limited currently. In non-collaborative scenarios with constrained receiver conditions, reconnaissance parties face challenges in acquiring prior knowledge and complete signal information, leading to difficulties in detecting TT&C signals amidst strong background signal interference in complex electromagnetic environments. This paper introduces an end-to-end TT&C signal detection model, TATR. As demonstrated in the empirical results shown in Figure 8, with an increase in training epochs, the losses in detection box coordinates and categories consistently decrease while detection AP continually improves. This clearly indicates that our proposed framework achieves effective TT&C signal detection under constrained conditions.  
From Table 4, it is evident that the proposed TATR model exhibits significant improvements in the [EMAIL_ADDRESS]:0.95 compared to the YOLO series models and Faster-RCNN. This can be attributed to TATR being an anchor-free detection framework that eliminates the post-processing step of non-maximum suppression. Inappropriate anchor settings can significantly degrade signal detection performance based on our years of experience in satellite signal observation and processing. Our network transforms the signal detection box filtering problem into a bipartite graph matching problem, utilizing the Hungarian algorithm to find the optimal matching results.  
It is worth noting that in Table 4, TATR slightly underperforms compared to the YOLO series models in AR, which is attributed to fixed anchors causing YOLO to sacrifice detection accuracy slightly to boost AR, resulting in some false alarms, as depicted in Figure 12. A comparison between TATR-4 Layer and TATR-6 Layer in AR performance reveals that increasing the layers of the encoder and decoder contributes to addressing this issue. However, balancing the high AR with the introduced computational overhead is a crucial focus for future optimization of TATR.  
Visualizing the detection box results, as shown in Figure 12, demonstrates that TATR provides more accurate detection localization and higher confidence, owing to its integration of triplet attention and multi-head self-attention mechanisms to capture global and local features of signal spectra, respectively. As indicated in Table 3, with similar signal  
Figure 12: Comparison of signal detection results among different models under different SNRs. (**a**) Represents the ground truth; (**b**) represents the detection result of the Faster-RCNN model; (**c**) represents the detection result of the YOLOv5 model; (**d**) represents the detection result of the TATR model.  
detection performance, compared to TATR-6 Layer, the parameter count of TATR-4 Layer model is reduced by 5.7 M. This reduction is attributed to triplet attention correlating three-dimensional information of signal spectra--amplitude, frequency, and channel--enhancing the feature extraction capability without introducing additional learnable parameters (the parameter quantity only increases by 0.003 M). This alleviates the issue of excessive model complexity caused by stacking multiple encoder and decoder layers. However, due to the need for the decoder architecture to use the inference from the previous inference as the input for the next inference in the inference stage, real-time detection inference performance is insufficient. This will be a key focus for our future research to optimize the TATR framework.