## 4 Simulation
### Performance Evaluation of Experiment 3  
In this experiment, we demonstrate the recognition capability of the SMR feature on three different emitters with different working modes. The working mode of radar emitters 1-3 are, respectively, PRI staggering, frequency agility, and PRI jittering, and the three radar emitters are placed in three directions close to each other. All testing samples were from the emitter sample repositories. The other parameters are shown in Table 6.  
Figure 8: Accuracy of REID by using SMR in different SNRs.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Pulse Train** & **PA** & **CF(MHz)** & **PW (\%)** & **PRI (\%)** & **Pulse Number** \\ \hline Pulse train 1 & 1 & 27 & 10 & 90 & 60 \\ Pulse train 2 & 1 & 32 & 10 & 90 & 60 \\ Pulse train 3 & 1 & 37 & 10 & 90 & 60 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Simulation parameters.  
Figure 9: Error probability of REID by using SMR in different SNRs.  
Convolution neural network (CNN) and support vector machine (SVM) are taken for REID. The parameter features selected for REID are CF, PRI, PW, and SMR, which were freely combined to form one-dimensional, two-dimensional, three-dimensional, and four-dimensional feature vectors for the classifier.  
In this experiment, the structure diagram of CNN [29] is shown in Figure 10.  
In the structure of CNN, we select the optimized network structure \"SC-2S-7C-2S-5-3\" in terms of reference [30]. In this network structure, it denotes the convolution kernel size of the convolution layer (C1) is 5, the downsampling multiple of the pooling layer (S2) is 2, the convolution kernel size of the convolution layer (C3) is 7, the downsampling multiple of the pooling layer (S4) is 2, the number of training iterations is 5, and the training data are divided into 3 segments. The training parameters of CNN are the learning rate is 0.01, the weight decay is 0.0005, and the momentum constant is 0.8. All experiment data are composed of 600 samples containing three radar emitters from the simulation samples. Three hundred and sixty samples were utilized for training, and the other samples were used for testing. The recognition results based on CNN by different dimensional feature vectors in different SNRs are shown in Figure 11.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Emitter** & **PA** & **CF(MHz)** & **AOA** & **PW (\(\mathbf{\mu_{\text{s}}}\))** & **PRI (\(\mathbf{\mu_{\text{s}}}\))** & **Pulse Number** \\ \hline Emitter 1 & 1 & 27 & 49.8\({}^{\circ}\) & 5 & 46, 50, 54 & 200 \\ Emitter 2 & 1 & 27, 33 & 50\({}^{\circ}\) & 8 & 50 & 200 \\ Emitter 3 & 1 & 33 & 50.2\({}^{\circ}\) & 5 & 50 \(\pm\) 5 & 200 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Simulation parameters.  
Figure 11: _Cont._  
Figure 10: Structure diagram of convolution neural network (CNN).  
In another SVM classifier [31], radial basis function (RBF) was selected as the kernel function, the parameter of the kernel function was \(\gamma=0.1\), the penalty coefficient was C = 2. The recognition results based on SVM by different dimensional feature vectors in different SNRs are shown in Figure 12.  
As can be seen from Figures 11 and 12, when using the CNN and SVM classifiers, the recognition results are similar. In different SNRs, when a one-dimensional feature vector is utilized for recognition, the recognition accuracy based on the SMR feature is higher than other feature parameters. When the multidimensional feature vectors are used for recognition, if the feature vector contains the SMR feature, the recognition accuracy is also higher. It is further verified that the SMR feature has a better recognition ability. Furthermore, because of the self-training process of CNN, when we have a emitter sample database, we should choose the CNN classifier first. However, when there is no sample database, the SVM classifier is preferred.