# Intelligent Detection Method for Satellite TT&C Signals under Restricted Conditions Based on TATR
## 6 Experiment and Results
### Experimental Results and Analysis
#### 6.3.1 Validity Analysis and Ablation Experiments  
In the case of training and testing TATR across all SNRs, the bounding box loss and signal classification losses for each detection box are illustrated in Figure 8a. The losses decrease continuously with an increase in training epochs, ultimately converging, which indicates that the Hungarian algorithm is capable of matching the predicted detection \begin{table}
\begin{tabular}{c c c} \hline Ground Truth\textbackslash{}Predicted Value & Positive & Negative \\ \hline Positive & True positive (TP) & False negative (FN) \\ Negative & False positive (FP) & True negative (TN) \\ \hline \end{tabular}
\end{table} 
Table 2: Signal detection confusion matrix. boxes with the ground truth boxes, validating the effectiveness of our proposed anchor-free signal detection framework. The AR and multiple mAP metrics values show a significant increase with the training epochs, as evident in Figure 8b. TATR achieves an [EMAIL_ADDRESS] surpassing 90% and AR exceeding 80% across all SNRs. Later, we present an ablation analysis to delve into the performance of TATR across various SNRs and the impact of different ratios of labeled training samples. The performance of TATR under different SNRs is illustrated in Figure 9a, where it is visually apparent that as the SNR increases, both the AR and mAP metrics exhibit continuous improvement. This trend indicates the robustness of our model to changes in SNR, with a notable detection performance of [EMAIL_ADDRESS] (\(>\)95%) even at \(-\)10 dB, attributed to the global and local attention mechanisms aiding the network in learning robust spectral features. Notably, a sharp decline is observed in [EMAIL_ADDRESS] and [EMAIL_ADDRESS]:0.95 when the SNR drops below \(-\)10 dB, while the [EMAIL_ADDRESS] metric shows relatively minor fluctuations, suggesting that at \(-\)15 dB the network can identify the signal occurrence but struggles to obtain precise bounding box coordinates. Furthermore, the quantity of labeled samples is considered a crucial factor in evaluating performance, as it is challenging to obtain under non-collaborative conditions. We investigated the performance of TATR across all SNRs with a proportion of labeled samples in the training dataset, only \(\chi=[5,10,20,30,40,60,80,100(\%)]\) samples have labels in the  
Figure 8: Performance of TATR effectiveness analysis experiment: (**a**) testing loss varies with training rounds, (**b**) testing AP/AR indicators vary with training rounds. Figure 9: Performance of TATR effectiveness analysis experiment: (**a**) AP/AR metrics vary with SNRs, (**b**) AP/AR metrics vary with the ratio of labeled samples. training set. As depicted in Figure 9b, the model's performance improves as the quantity of available labeled samples increases. The performance of the model did not show a significant decrease until \(\chi=5\), indicating that TATR can maintain good performance under different labeled sample sizes. Subsequently, we conducted ablation experiments on the global and local attention mechanisms of the TATR model, comparing the performance with and without the triplet attention mechanism, as well as altering the number of Transformer encoder and decoder layers (2, 4, and 6 layers). The results are summarized in Table 3. It is evident that the detection performance of TATR improves with an increasing number of encoder and decoder layers. However, along with the increase in encoder and decoder layers, the total parameters and inference time of the network also increase simultaneously, because the multi-head self-attention mechanism reloaks keys and values repeatedly during the inference phase, posing a balance issue between speed and accuracy when stacking too many layers. The ResTA block effectively mitigates this issue. It is evident that, with layers 4 and 6, the results of adding the triplet attention module consistently outperform those relying solely on encoder and decoder for local attention in associating spectrum features.