# Radar Emitter Structure Inversion Method Based on Metric and Deep Learning
## 4 Res Inversion Method  
As observed in the above modeling, different structure design parameters and operating parameters can produce various RF signals that carry different RF structure information and signal information, or they map the parameter domain to the data domain. If the data domain can be mapped to the feature domain so that the neural network can learn the relevant features representing the RF structure information and signal information, it will be conducive to the RES inversion.  
Then, by comparing the channel system [56], the RES can be regarded as an RF structure system, \(RFS(\cdot)\), which carries RF structure information. Different classes of RES correspond to different RF structure systems because they are composed of different structure design parameters and carry different RF structure information. This is a complex nonlinear and unknown system; the specific parameters of the system cannot be obtained via simple calculations.  
The ideal RF signal \(\mathbf{x}\) without RF structure information, that is, \(RFS(\mathbf{x})=\mathbf{x}\), can be considered as the baseband signal \(\mathbf{s}\) with different operating parameters after passing through the modulation system \(MT(\cdot)\):  
\[\mathbf{x}=MT(\mathbf{s}). \tag{4}\]  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Module** & **Design Parameter** & **PA1** & **PA2** & **PA3** \\ \hline \multirow{4}{*}{Mixer (Phase Noise)} & Frequency Offset (Hz) & FO1 = [100 5000 30,000 50,000] FO2 = [100 1000 100,000 100,000] \\ \cline{2-4}  & Phase Noise Leve (\(\mathrm{d}\mathrm{Bc}/\mathrm{Hz}\)) & \(\mathrm{P}\mathrm{N}\mathrm{L}\mathrm{1}\mathrm{1}\mathrm{1}  
Therefore, the RF signal \(\mathbf{y}\) carrying RF structure information can be considered as the ideal RF signal obtained via the RF structure system:  
\[\mathbf{y}=RFS(\mathbf{x})=RFS(MT(\mathbf{s})). \tag{5}\]  
Based on the above analysis, a dual neural network is designed in this section. An AE network aims to train a feature encoder that is capable of extracting deep signal features in an unsupervised manner using ideal RF signals. As for the AEM network, the feature encoder of the AE network is used to extract the deep signal features, constrain the AEM network in the feature domain, and reconstruct the RF signal in an unsupervised manner in order to learn the potential RF structure features that can stably characterize the RES from the data domain. The purpose of this step is to map the data domain to the feature domain and back to the data domain via the powerful nonlinear processing ability of the neural network, thereby mapping the RF structure information to the deep RF structure features one by one. Then, a metric scorer is designed using metric learning, and the AEM network's structure and objective function are optimized end-to-end, resulting in deep RF structure features for each class of RES that are close to the centroid of the deep RF structure features of the same class of RES and far from the centroid of the deep RF structure features of other classes of RES. During the learning process, the network shortens the distance between the RF signals of hetero-mode operating parameters in the same class of RES in the feature domain and widens the distance between RF signals in the feature domain of different classes of RES. This dual neural network is described in detail in the sequel. To illustrate the RES inversion method in the simplest form, consider a deep neural network as an example, where each module contains two hidden layers. In specific cases, one can choose more complex neural networks, such as convolutional neural network (CNN) or long short-term memory (LSTM) [57].