# Radar Emitter Structure Inversion Method Based on Metric and Deep Learning
## 2 Problem Definition
Next, the influence of the change in operating parameters upon RES inversion is analyzed. Question 1: Training data question: Although a large amount of training data can be obtained under certain known operating parameters (e.g., the frequency is 25 MHz and 26 MHz), it is not possible to collect all training data within all changing ranges of operating parameters (e.g., the frequency is 27 MHz). Additionally, samples with different operating parameters are often imbalanced (for example, there are more samples at 25 MHz than at 26 MHz) due to the limitations of the working scene, parameter selection and data collection conditions, and other factors. Therefore, a complete database cannot be easily established using electronic reconnaissance systems. Question 2: The influence of hetero-mode operating parameters: With the improvement in the circuit integration process, the difference in nonlinear characteristics between components is decreasing, but radar operating parameters are constantly changing, and the difference gradually increases. As a result, if the same class of RES transmits RF signals with different operating parameters, the signal information carried by the RES may cover the RF structure information, or it may completely exceed the RF structure information. If different classes of RES transmit RF signals with different operating parameters, the signal information carried by the different classes of RES may exceed the RF structure information that is carried. As a result, the neural network might pay more attention to each signal information, which results in the performance of the neural network being significantly affected by the change in operating parameters. When there are different REIs under the same class of RES, difficulties in classification greatly increases under hetero-mode operating parameters. Question 3: The influence of unknown operating parameters: When receiving an RF signal with unknown operating parameters transmitted by a known RES, the neural network maps the training data domain to the feature domain completely according to the known operating parameters and determines the classification boundary. When the operating parameters of the received RF signal are not within the operating parameters range of the training data, can this neural network be used to map the RF signal with unknown operating parameters to the feature domain of known operating parameters? Will the neural network make false judgments? Question 4: The influence of noise: When noise levels are high in the real environment, both the RF signal and structure information may be covered at the same time. It is unknown whether noise will cause the neural network to be unable to extract meaningful features, thereby making the performance of the neural network more sensitive to noise and less robust. Therefore, the problem to be solved in this paper is to extract the good stability and separability of features that represent RF structure information under this hetero-mode operating parameter; then, this feature is used to invert the RES and make the neural network insensitive to changes in the operating parameters.