# Intelligent Detection Method for Satellite TT&C Signals under Restricted Conditions Based on TATR
## 5 Methods
### ResTA Spectrum Feature Extraction Network  
Detection models based on deep learning typically utilize CNNs as a backbone. However, CNNs, constrained by the size of convolutional kernels, often face challenges in correlating global information, which is detrimental for tasks requiring full-spectrum detection. To address this, we integrate the triplet attention mechanism [10] into the ResNet50 [45] and propose the ResTA backbone, which serves as the spectral feature extraction network for TATR. ResTA globally correlates the height, width, and channel dimensions of features, divided into five parts. The first part, without residual blocks, primarily performs convolution, normalization, activation, and max-pooling operations on the input. The second, third, fourth, and fifth parts feature stacked ResTA blocks. The ResTA block structure, illustrated in Figure 5, involves three convolutional layers followed by the triplet attention module to capture spectral attention. Additionally, it incorporates a residual connection mechanism across layers to mitigate the impact of gradient vanishing. The triplet attention module, as illustrated on the right-hand side of Figure 5, computes attention weights by capturing cross-dimensional interactions using a three-branch structure. For an input tensor \(\vartheta\), the triplet attention module establishes dependencies between dimensions through rotation operations and residual transformations, encoding information across channels and spatial dimensions with negligible computational overhead. Specifically, triplet attention consists of three parallel branches. The first branch is responsible for capturing cross-dimensional interactions between channel \(C\) and spatial dimension \(H\), achieving this by rotating the input spectral feature \(\vartheta\in R^{C\times H\times W}\) counter-clockwise by 90\({}^{\circ}\) along the \(H\)-axis, resulting in tensor \(\vartheta_{r}\in R^{W\times H\times C}\), which undergoes a Z-Pool operation to transform into \(\vartheta_{r}\in R^{2\times H\times C}\). The Z-Pool operation is represented as  
\[Z-Pool(\vartheta_{r})=[MaxPool(\vartheta_{r}),AvgPool(\vartheta_{r})] \tag{6}\]  
Figure 4: The overall network structure of TATR. This network consists of three parts: ResTA backbone network for spectral feature extraction; neck with multilayer encoders and decoders; and signal detection head block, including class loss and bounding box loss. Firstly, the 1D spectrum amplitude sequence is transformed into a 2D spectrogram and fed into the ResTA backbone. Subsequently, the position embedding of the spectrogram is jointly utilized as input for the TATR encoder and decoder. Finally, the output of the TATR decoder undergoes FFN mapping to derive the positional coordinates and parameter information of the signal. Subsequently, a standard convolution layer with a kernel size of \(k\times k\) is applied to the Z-Pool to reduce its \(W\) dimension to 1. Finally, the attention weights are generated through a sigmoid activation, and the resulting attention feature \(\theta_{att-1}\in R^{C\times H\times W}\) from the first branch is obtained by multiplying it by the input spectral feature tensor. The second branch is responsible for capturing cross-dimensional interactions between channel \(C\) and spatial dimension \(W\), while the third branch captures spatial attention between the spatial dimensions \(H\) and \(W\). They go through Z-Pool operations, standard convolution layers, and sigmoid activations to generate attention weights. The resulting attention features, denoted as \(\theta_{att-2}\in R^{C\times H\times W}\) and \(\theta_{att-3}\in R^{C\times H\times W}\), are obtained by multiplying them by the corresponding input tensors.