# Radar Emitter Structure Inversion Method Based on Metric and Deep Learning
## 5 Experiments
### Comparison and Analysis of the Generalization Performance of Different Networks  
Since RES inversion is a new problem, there are no comparable methods in the published literature. Therefore, in this section, the generalization performance of the AEM network is evaluated via experimental comparisons with some relevant baseline neural networks. Due to space limitations, only some of the experimental results from the test dataset are shown. The baseline network1 is a deep neural network (DNN): it consists of a feature encoder with two hidden layers and a SoftMax classifier (i.e., a feature encoder and a classifier without a reconstruction decoder); the baseline network2 is a CNN: it includes three convolutional and pooling layers, two DNN layers, and a SoftMax classifier. These two networks are used to demonstrate the role of including a reconstruction decoder in this AEM network. Another network used for comparisons is the SAE network: it consists of three stacked AE networks and a SoftMax classifier (i.e., a feature encoder, a reconstruction decoder, and a classifier). The network is trained by initializing and fine-tuning layer by layer. This network is used to demonstrate the function of the metric scorer in the AEM network. Except for the differences in the network's structure design, the other conditions are the same as the proposed AEM network. Figure 14: Proof by contradiction: (**a**) visualization; (**b**) inversion confusion matrix. Figure 13: Visualization of deep RF structure features relative to different modulation types: (**a**) CW; (**b**) BPSK; (**c**) LFM. In the closed-set test dataset, samples with \(\text{SNR}=10\) dB are selected to verify the performance of the comparison network and the proposed AEM network. Figure 15a-d visualizes the four networks using t-SNE technology to reduce the extracted deep RF structure features in two-dimensional space. It can be observed that the features extracted by the four networks are distinguishable. However, since the DNN, CNN, and SAE networks use the SoftMax classifier, the spacing between the same class is large, and the boundary of different classes is blurred. The AEM network makes the distance between the same class smaller, the distance between different classes larger, and the boundary clearer using metric learning. Therefore, features extracted by the AEM under hetero-mode operating parameters have higher separability, and samples mixed with three different modulation types of the same class of RES are grouped together. Figure 15e-h illustrates the inversion confusion matrix of the four networks. As observed, all four networks exhibit good inversion performance. Due to the lack of reconstruction performance in DNN and CNN networks, Figure 16a,b only shows the time-domain reconstructions of SAE and AEM networks. It can be observed that the SAE network has poor reconstruction performance. The AEM network has good reconstruction performance, and it can constrain the features to make them more stable. In the open-set test dataset4, samples with \(\text{SNR}=10\) dB are selected to verify the performance of the comparison network and the proposed AEM network under unknown operating parameters (MT). Figure 17a-d visualizes the four networks using t-SNE technology to reduce the extracted deep RF structure features in two-dimensional space. It can be observed that in DNN, CNN, and SAE networks, the distance between elements of the same class is large, the boundary between different classes is fuzzy, and even aliasing occurs between classes.