# Radar Emitter Structure Inversion Method Based on Metric and Deep Learning
## 5 Experiments
### Feature Extraction Performance and Analysis of the AEM Network  
First, to verify whether the AEM network can extract the RF structure information carried by RF signals under hetero-mode operating parameters, the centroids of the deep RF structure features of three classes of RES saved by the AEM network after training are visualized and analyzed in the form of a heat distribution map [59]. Figure 12 shows a visualization of the 50-dimensional deep RF structure feature centroid combination of three classes of RES extracted from the AEM network. It can be observed that the centroids have obvious differences. The feasibility and uniqueness of extracting deep RF structure features from the AEM network are verified. Therefore, these deep RF structure features can be used for RES inversion.  
Sample data with \(\text{SNR}=10\) dB were selected in the closed-set test dataset to further verify whether the deep RF structure features extracted by the AEM network in this paper have good separability under hetero-mode operating parameters. The deep RF structure features extracted by the AEM network have reduced dimensionality using t-SNE [28] technology, and the distribution of deep RF structure features is visualized in two-dimensional space. Figure 13a-c show the visualization of the deep RF structure feature distribution of three classes of RES extracted by the AEM network using CW, BPSK, and LFM modulation types, respectively. It is clear that these RF structure features have obvious separability relative to different modulation types, and the separability effect is the best under the LFM modulation type.  
Finally, the stability of the deep RF structure features extracted using the AEM network was further verified under hetero-mode operating parameters. The ideal test dataset without RF structure information under the operating parameters corresponding to the closed-set test dataset (\(\text{SNR}=10\) dB) was simulated and input into the AEM network to verify its separability and inversion rate. This is a mathematical paradox; if the AEM network cannot clearly distinguish between ideal test data without RF structure informa  
Figure 12: Visualization of 50-dimensional deep RF structure feature centroids of three classes of RES using the AEM network.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Test Dataset** & **F (MHz)** & **B (MHz)** & **P (mW)** & **MT** \\ \hline Closed Set & 25\(\sim\)30 & 10\(\sim\)15 & 0.1, 0.5, 1 & CW, LFM, BPSK \\ \hline Open Set1 (F) & 31\(\sim\)32 & — & 0.1, 0.5, 1 & CW \\ \hline Open Set2 (B) & 25\(\sim\)30 & 16\(\sim\)17 & 0.1, 0.5, 1 & LFM \\ \hline Open Set3 (P) & 25\(\sim\)30 & — & 0.3, 0.7 & BPSK \\ \hline Open Set4 (MT) & 25\(\sim\)30 & — & 0.1, 0.5, 1 & QPSK \\ \hline Open Set5 (MT, F, and P) & 31\(\sim\)32 & — & 0.3, 0.7 & QPSK \\ \hline \hline \end{tabular}
\end{table}
Table 3: Operating parameters of the test dataset.  
tion, this indirectly proves that it is extracting deep RF structure features rather than deep signal features. Figure 14a is a visualization of a two-dimensional space that uses t-SNE technology to reduce the dimension of extracted features. It can be observed that using the ideal test dataset, the features extracted by the AEM network are not separable. Figure 14b shows the inversion confusion matrix, where it can be observed that the AEM network no longer has the inversion performance. Therefore, the feasibility and stability of deep RF structure features extracted by the AEM network were indirectly proven.