## 1 Introduction  
Specific emitter identification (SEI) is a process to extract individual features from signals of the same model and batch of communication emitters and identify the specific emitter [1]. The manufacturing process of emitters is random, so emitters, even of the same model and batch, do not have completely identical electrical characteristics [2]. With different electrical parameters, each emitter has unique characteristics and is thus called a fingerprint. The fingerprint characteristics [3] of the signal are due to the effects of in-phase and quadrature-phase imbalance(IQ imbalance), phase noise, harmonic distortion, and nonlinear distortion [4]. As the fingerprint of an emitter is unique, steady, and difficult to imitate, SEI based on the fingerprint feature extracted from an emitter is an effective method to identify the identity of the communication emitter. The SEI methods can be divided into two categories: manual-feature-based methods and deep learning-based methods. Manual-feature-based methods extract manual features from the emitter signal and then use the machine learning classifier to Identify the specific emitter. Different from manual-feature-based methods, the deep learning-based methods automatically extract the features of the specific emitter signal to perform specific emitter identification. At present, various methods have already been proposed for extracting the features of an emitter signal [5]. Specifically, the features obtained by spectral analysis of the signal include power spectrum features [6], frequency spectrum features [7], Hilbert spectrum features [8], and variational mode decomposition spectrum features [9]. The features generated in modulation-demodulation include non-linear features of the power amplifier [10],phase error, IQ offset [11], and carrier frequency offset [12; 13]. Moreover, there are also many other emitter signal feature extraction and identification methods. For example, in [6], SEI was realized by extracting the signal's fractal feature. In [14], the authors put forward an FID model for mathematical modeling of an emitter based on the emitter type and used this model to identify the undetermined signal. [PERSON] et al. clustered IQ signals directly to identify specific emitters [15]. However, the manual feature extraction-based method has many shortcomings: First, the SEI system's identification effect is limited by the effectiveness of the manual feature; additionally, the extraction of the manual feature necessitates that the researchers have sufficient prior knowledge of communication theory on emitters [4]. Second, this method has poor generalizability. A feature extraction method valid for the identification of one emitter is often invalid for the identification of other emitters. Hence, researchers usually need to find a new feature extraction method for identifying a new emitter. Third, many traditional SEI methods perform poorly and cannot meet the practical demands. Many recent studies have shown that deep learning outperformed traditional methods in SEI [16]. The shortcomings of traditional methods are being solved by deep learning. Deep learning is undergoing rapid development and has been widely used in image processing, natural language processing, and speech recognition, obtaining excellent results. Currently, researchers are applying deep learning technology to SEI. Furthermore, some study results have proved that deep learning is feasible and has great potential in SEI. For example, [PERSON] et al. [17] systematically analyzed the studies on deep neural networks in radio modulation identification. [PERSON] et al. [7] identified 22 LoRa devices in the multi-layer perceptron (MLP) and convolutional neural network (CNN) supervised learning methods. [PERSON] et al. [18] identified five 802.11 protocol emitters by using CNN. Following that, [PERSON] et al. identified 25 LoRa emitters by using CNN, MLP, and Long Short-Term Memory (LSTM), respectively.