## 4 Experiments and Results
### Network Performance Test  
Datasets EE, FF, and GG were used to test the performance of the Siamese network to recognize new targets with different parameters. * Performance evaluation of Doppler shifts. Dataset EE was used to assess the performance of the Siamese network to recognize new targets with different Doppler shifts. Positive and negative sample pairs were input to test the network accuracy for moving targets with different velocities. The results are shown in Figure 5. The horizontal axis is the relative Doppler shift \(v/c\)[31], and the vertical axis is the accuracy of the Siamese network. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Layer Type** & **Neuron Size** & **Output Shape** \\ \hline Input & & 2048 \(\times\) 1 \\ Dense\_1 & 1024 & 1024 \(\times\) 1 \\ Dense\_2 & 256 & 256 \(\times\) 1 \\ Dropout\_1 & & 256 \(\times\) 1 \\ Dense\_3 & 128 & 128 \(\times\) 1 \\ Dropout\_2 & & 128 \(\times\) 1 \\ Dense\_4 & 64 & 64 \(\times\) 1 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The parameters of the proposed Siamese network with MLP. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Layer Type** & **Output Shape** & **No. Kernels** & **Kernel Size** & **Activation Function** \\ \hline Input & 2048 \(\times\) 1 & & & \\ Conv\_1D & 2048 \(\times\) 4 & 4 & \(1\times\) 5 & ReLU \\ MaxPooling1D & 512 \(\times\) 4 & 1 & & \\ Conv\_1D & 512 \(\times\) 4 & 4 & \(1\times\) 5 & ReLU \\ MaxPooling1D & 128 \(\times\) 4 & 1 & & \\ Conv\_1D & 128 \(\times\) 4 & 4 & \(1\times\) 5 & ReLU \\ MaxPooling1D & 32 \(\times\) 4 & 1 & & \\ Flatten & 128 \(\times\) 1 & & & \\ Dense & 64 \(\times\) 1 & & & ReLU \\ \hline \hline \end{tabular}
\end{table} 
Table 3: The parameters of the proposed Siamese network with CNN. Figure 5 shows the high accuracy of the network for 10 types of new targets and 41 Doppler shifts. The result shows that the proposed network with CNN can recognize new targets with different Doppler shifts. The recognition rate of the Siamese network with MLP is relatively low. To further evaluate the recognition performance with dataset EE, we analyzed the results of samples with the maximum relative Doppler shift (\(f_{d}\) = 0.02). The classification results are shown in Figure 6, where the vertical axis is the actual label, and the horizontal axis is the predicted label. There are 10 types of targets, and the number of samples in each type is 1000. The color depth reflects the accuracy of recognition. Precision [32] is defined here as the probability of being a positive sample among all predicted positive samples, which can be expressed as  
\[precision_{n}=\frac{TP_{n}}{TP_{n}+FP_{n}} \tag{13}\]  
\"Recall\" [33] is the probability of being predicted as a positive sample among the actual positive samples, which can be expressed as  
\[recall_{n}=\frac{TP_{n}}{TP_{n}+FN_{n}} \tag{14}\]  
Figure 5: The performance of the proposed Siamese network for Doppler shifts. Figure 6: The classification results of samples with \(f_{d}\) = 0.02.  
where \(n\) is the sample label of the \(n\)-th type, \(TP\) represents true positive, \(FP\) denotes false positive, and \(FN\) is false negative. We introduced a new measure of balance between them: the \(F1\)-score [34]. The \(F1\)-score considered both the precision rate and the recall rate so that the two can reach their maxima simultaneously and achieve a balance. The \(F1\)-score is expressed as  
\[F1_{n}=\frac{2*\textit{precision}_{n}*\textit{recall}_{n}}{\textit{ precision}_{n}+\textit{recall}_{n}} \tag{15}\]  
By calculating the average \(F1\)-score of each type, \(F1_{\textit{average}}\) can be written as  
\[F1_{\textit{average}}=\frac{1}{N}\sum_{n=0}^{N-1}F1_{n}, \tag{16}\]  
where \(N\) is 10. When the Doppler shift (\(f_{d}\) = 0.02) was at maximum, the calculated value of \(F1_{\textit{average}}\) was 0.8462. Dataset FF was used to evaluate the performance of the Siamese network to recognize new targets with 16 types of SNRs.