## 4 Proposed Method
### Improved Attention Feature Fusion: CAF  
Feature fusion can effectively utilize the complementary characteristics between multiple features to enhance network performance. However, current research on feature fusion in the field of radar human activity recognition mostly remains at the level of simple addition, multiplication, or concatenation. These operations only provide fixed linear fusion, leaving us uncertain whether a combination is suitable for specific objects. Attention feature fusion (AFF) provides us with a dynamic and selective solution. Figure 7 illustrates the implementation process of AFF. Global pooling in SE attention is used to encode channel information, but this global compression method cannot perform spatial encoding. Coordinate attention (CA) is a clever improvement on SE attention, allowing it to not only focus on channel features but also accurately capture the location information of the target. Specifically, this method decomposes the global pooling operation into two 1D pooling operations. Pooling operations with kernel sizes of \(H\times 1\) and \(1\times W\) are applied along the vertical and horizontal \begin{table}
\begin{tabular}{c c c} \hline \hline
**Sub-Module** & **Expansion Layer Channels** & **Output Channels** \\ \hline Conv2D & None & 16 \\ Ghost-Bottleneck1 & 16 & 16 \\ Ghost-Bottleneck2 & 48 & 24 \\ GhostV2-Bottleneck2 & 72 & 40 \\ GhostV2-Bottleneck2 & 120 & 80 \\ GhostV2-Bottleneck2+SE & 160 & 80 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: The number of channels for each block of TG2. Figure 6: The structure of the TG2 feature extraction module. The diagram consists of four columns: the first column represents the overall structure of TG2, and the remaining three columns illustrate the specific contents of the modules within TG2. Notably, the SE module enclosed by a dashed line in GhostV2 Bottleneck2 indicates that this module is optional. directions, respectively, to aggregate and generate the spatial coordinate encoding on the x-axis and y-axis for the \(c\)-th channel feature map:  
Let \(X_{1}\) and \(X_{2}\) be the high-level features of the two types of spectrograms, and \(f_{att}\) be the channel attention. Then, the output after AFF can be represented as [25]:  
\[Y_{AF}=f_{att}(X_{1}\oplus X_{2})\odot X_{1}\oplus(1-f_{att}(X_{1}\oplus X_{2}) )\odot X_{2}. \tag{11}\]  
Unless specifically required by the task, channel attention mechanisms are an excellent choice within the AFF modules. In fact, radar spectrograms do not exhibit spatial invariance like visual images [29]. When observing the same target object, the spatial distribution of radar spectrograms does not change significantly, and this spatial information carries substantial physical significance. Therefore, it is necessary to improve AFF by extending weighted fusion to include spatial distribution. Global pooling in SE attention is used to encode channel information, but this global compression method cannot perform spatial encoding. Coordinate Attention (CA) is a clever improvement on SE attention, allowing it to not only focus on channel features but also accurately capture the location information of the target. Specifically, this method decomposes the global pooling operation into two 1D pooling operations. Pooling operations with kernel sizes of \(H\times 1\) and \(1\times W\) are applied along the vertical and horizontal directions, respectively, to aggregate and generate the spatial coordinate encoding on the x-axis and y-axis for the \(c\)-th channel feature map:  
\[ \begin{split} z_{c}^{w}(w)&=\frac{1}{H}\sum_{i=1}^ {H}x_{c}(i,w),\\ z_{c}^{h}(h)&=\frac{1}{W}\sum_{j=1}^{W}x_{c}(h,j). \end{split}  \tag{12}\]  
where \(x_{c}\) denotes the input feature of the \(c\)-th channel.