# Intelligent Detection Method for Satellite TT&C Signals under Restricted Conditions Based on TATR
## 5 Methods
### TATR Encoder and Decoder  
The TATR encoder and decoder is established upon the encoder and decoder architecture of the Transformer model [35]. This structure entirely eliminates CNNs and RNNs, opting for self-attention mechanisms for both encoding and decoding to establish connections among local features in the temporal sequence of signal spectrograms. The architecture of the TATR encoder and decoder is depicted in Figure 6.  
Figure 5: ResNet with triplet attention module (ResTA block). This structure divides the output of each block in ResNet into three channels. These channels then undergo rotation, attention calculation, and stacking to associate signal characteristics across channels, amplitude, and frequency dimensions. The TATR encoder module is composed of stacked identical encoders. Each encoder consists of multiple self-attention layers and two feed forward network (FFN) layers. The spectrogram features obtained from the ResTA spectrogram feature extraction network are first processed by a convolutional layer with a kernel size of \(1\times 1\), reducing the channel dimension from \(C\) to \(C_{1}\). The spatial dimensions are then folded into a single dimension to obtain a new feature map \(F_{1f-img}\in R^{C_{1}\times(H_{1}\times W_{1})}\), which serves as part of the input to the encoder. However, unlike 1D sequences, the envelope variation information of 2D spectrograms requires guidance on frequency positions. Therefore, we apply positional embedding to the signal to help the network learn the correlation between different frequency positions of the signal and integrate them with global attention features as inputs to the TATR encoder. These inputs are transformed into the required query, keys, and values vectors using three weight matrices: \(W^{Q},W^{K}\), and \(W^{V}\). Then, a self-attention operation is performed:  
\[attention(Q,K,V)=\text{softmax}(\frac{QK^{T}}{\sqrt{d_{k}}})V \tag{8}\]  
where softmax denotes the normalized exponential function, \(Q\) represents the query vector, \(K\) is the keys vector, \(V\) stands for the values vector, and the dot product of \(Q\) and the transpose of \(K\) yields the attention scores for each word vector. \(d_{k}\) is the dimensionality of the keys vector. As every vector in the input signal features undergoes self-attention, forming a direct connection between any two vectors, the self-attention mechanism can learn the correlation between frequency points in TT&C signals in a wideband reception scenario. The overall architecture is similar to the encoder, with the difference that the decoder adds a masked-multi head self-attention layer, where the mask ensures that the prediction at position \(i\) depends only on the outputs before position \(i\). In signal detection, the decoder outputs results through an autoregressive process. The structure of the TATR decoder is depicted on the right-hand side of Figure 6.  
The decoder receives self-attention vectors from the encoder as the values vector for the decoder layer. The combination of satellite signal position encoding and self-attention vectors serves as the keys vector, and the encoded satellite signal detection box acts as the query vector. The self-attention operation on the query, keys, and values vectors is expressed in Equation (8). Similar to the encoder, the result goes through a residual connection, followed by layer normalization, which calculates the mean and variance across different channels for each sample to address the issues of gradient vanishing and weight matrix degradation. The multi-head self-attention mechanism facilitates the network in automatically selecting relevant contextual information in latent space for  
Figure 6: TATR encoder and decoder structure. Structure of the encoder (**left**); structure of the decoder (**right**). The output of the encoder jointly uses the query volume as the input for the decoder. detection purposes (this will be empirically demonstrated and extensively discussed in the experimental section).