# 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
This is attributed to the triplet attention module capturing global attention features during the spectrum feature extraction phase, alleviating the subsequent encoder and decoder's local attention learning overhead. Notably, the ResTA block introduces almost no additional learning parameters. The 4-layer TATR with the triplet attention module and the 6-layer TATR without the triplet attention module show similar detection performance. However, the former exhibits a reduction of almost 14% in model parameters and a 36% improvement in inference speed. Therefore, the ResTA block in the proposed TATR model can associate global channel, frequency, and amplitude information, enhancing the detection performance of the model across multiple metrics while reducing the model's parameter count. Furthermore, we analyze the performance of the detection indicators of the TATR model at different SNRs, as shown in Figure 10. Figure 10a-d, respectively, display the curves of the detection metrics [EMAIL_ADDRESS], [EMAIL_ADDRESS]:0.95, [EMAIL_ADDRESS], and AR with varying SNRs. Overall, TATR-6 Layer achieves nearly the best performance at various SNRs. When the SNR is greater than \(-5\) dB, [EMAIL_ADDRESS] reaches close to 100%, and the AR exceeds 80%, demonstrating the robustness of the proposed TATR model across different SNRs. It is noteworthy that the ResTA block shows a more significant improvement in the model with two and four encoder and decoder layers, with [EMAIL_ADDRESS]:0.95 increasing by 4.8-14.7% and 4-15%, respectively. In contrast, on the TR-6 Layer model, the improvement is only \(-1\)-9.3%, indicating that the global attention capability of the ResTA backbone can extract more effective features, aiding subsequent self-attention modules in obtaining spectrum information and signal position detection. However, the performance of TATR-2 Layer is \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **TA Block** & **[EMAIL_ADDRESS] (\%)** & **[EMAIL_ADDRESS]:0.95 (\%)** & **[EMAIL_ADDRESS] (\%)** & **AR (\%)** & **Parameters (M)** & **FPS** \\ \hline TR-2 Layer & ✗ & 27.93 & 19.05 & 11.24 & 53.57 & 29.70 & 39 \\ TATR-2 Layer & ✗ & 35.84 & 26.86 & 20.04 & 62.45 & 29.70 & 39 \\ \hline TR-4 Layer & ✗ & 92.21 & 47.37 & 44.98 & 73.62 & 35.49 & 30 \\ TATR-4 Layer & ✗ & 95.78 & 56.82 & 50.21 & 79.67 & 35.49 & 30 \\ \hline TR-6 Layer & ✗ & 95.81 & 56.45 & 51.23 & 79.81 & 41.28 & 19 \\ TATR-6 Layer & ✗ & **96.84** & **60.21** & **53.23** & **82.23** & 41.28 & 19 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance of indicators for global and local attention module ablation in TATR ([EMAIL_ADDRESS] represents average precision at IoU threshold of 0.5, TA Block column represents whether triplet attention (TA) module is added, ✗ represents TA module added, ✗ represents TA module not been added). poor and insufficient to support the capture of spectral features. Therefore, TATR-4 Layer and TATR-6 Layer may be considered as two model versions for practical deployment.