# Radar Emitter Structure Inversion Method Based on Metric and Deep Learning
Thus, the main problem of these methods involves the extraction of the RF features of the RF signal directly. However, these methods do not pay attention to the interference introduced by signal information. Does the extracted RF feature contain a large amount of signal information? Therefore, the problem to be solved in this paper is as follows: how can we reduce the interference of signal information in complex situations so that the extracted RF structure features have good stability and separability? Moreover, how can these RF structure features be used to achieve RES inversion while retaining the RES inversion algorithm's good robustness and generalization performance under unknown operating parameters? As far as we know, there are no relevant papers that have examined this issue. It is worth noting that this complexity includes the complexity of RES modeling (we invert the twelve radar emitters into three classes of structures) and the complexity of the variable operating parameters (modulation type, frequency, bandwidth, and input power). In summary, it is challenging to realize RES inversion in complex situations, and the traditional method can easily fail due the to interference of signal information due to the lack of influence of signal information under variable parameters on extraction of RF features. In the field of radar signal recognition, the closed-set problem of signal modulation type recognition refers to the recognition of known modulation type signals, while the open-set problem refers to the recognition of unknown modulation type signals [42], and its open-set problem is more challenging. Researchers have introduced metric learning into signal modulation type recognition and demonstrated the advantages of triplet loss functions in feature extraction [28]. This paper conducts an RES inversion study on closed-set (knownoperating parameters) and open-set (unknown operating parameters) problems using hetero-mode operating parameters, which combines metric learning and deep learning techniques to study how effectively to reduce the interference caused by signal information. We attempt to find methods that can render the neural network more attentive toward the common RF structure information of RF signals among these hetero-mode operating parameters in the learning process; we also attempt to render the neural network insensitive to changes in terms of operating parameters, and we provide good stability and separability for extracted RF structure features, which is the key to RES inversion. Figure 1 shows the overall flowchart of the method proposed. First, RF signals are obtained using RES modeling. Then, the AEM network is constrained in the feature domain using the deep signal features extracted from the AE network in the data domain, and the RF signal is reconstructed as unsupervised to weaken the signal information in the feature domain of the RF signal under hetero-mode operating parameters; RF structure information is highlighted. Distinct from traditional algorithms, the RF structure features automatically extracted via deep learning are called \"deep RF structure features\", and the signal features are called \"deep signal features\" in this paper. The main contributions of this paper are summarized as follows:  
1. Stability: A dual neural network (AE and AEM network) is designed based on the idea of unsupervised learning. The use of deep learning techniques to reduce the interference caused by signal information is essential for extracting RF features that are not sensitive to parameter variations. This idea is instructive for RF feature extraction techniques under variable operating parameters. 2.