# Open-Set Specific Emitter Identification Based on Prototypical Networks and Extreme Value Theory
## 1 Introduction  
The large-scale application of wireless communication technology and the advent of the Internet of Things era have prompted the rapid development of a large number of communication devices, along with serious challenges in the fields of communication security and signal reconnaissance. Traditional MAC address and secret key authentication methods are easy to forge and crack, and relying on signal analysis methods to track and identify signals can no longer meet the needs of reconnaissance in complex electromagnetic environments. Specific emitter identification (SEI) can accomplish the authentication and identification of individual emitters by extracting the inherent fingerprint features in signals of different emitters due to hardware manufacturing and other effects [1]. In the field of electronic reconnaissance, the identification of individual features can effectively provide information on non-cooperative targets, track the spatial location of signals, analyze the electromagnetic situation on battlefields, and generate valuable intelligence. In addition, SEI combined with traditional authentication methods, such as secret key and MAC address, can strengthen the recognition and identity authentication of illegal wireless communication devices [1,2] and improve the security performance of communication systems. According to the working state of the communication emitters, the signal fingerprint features can be divided into transient features and steady-state features. The differences in transient features are obvious and easy to distinguish, but the extraction of transient features requires high-precision equipment and acquisition conditions, since it is susceptible to noise [3, 4, 5, 6]. In view of the difficulty of detecting the transient starting point of the signal,Ref. [7] several methods for detecting transient starting points have been developed. Among them, the energy criterion method based on the instantaneous amplitude characteristics was recently shown to be superior. Compared with transient features, steady-state features are easy to obtain; therefore, the method of extracting RF fingerprints based on steady-state features has been widely researched and applied. For example, the high-order cumulant detection function was constructed to extract the boot signal envelope, and the envelope fractal feature was used to cluster the signal extraction results, which could identify the fingerprinting characteristics of FH radio stations [8]. The fractal features of the FH signal extracted by this method could effectively suppress the noise impact. Ref. [9] proposed square integral bispectra (SIB) to extract the unique stray features of an individual transmitted signal, and the principal component analysis (PCA) method was utilized to extract a low-dimensional classification vector. Then, a support vector machine (SVM) based on the Gaussian kernel function was implemented to complete the classification. The proposed model was highly accurate and robust even in the presence of excessive noise. Ref. [10] proposed an emitter identification based on variational mode decomposition and spectral features (VMD-SF). This method had a lower computational cost than the VMD-_EM\({}^{2}\)_ method and the existing EMD-_EM\({}^{2}\)_ method. However, these feature extraction and classification methods have high complexity. The extracted features are not comprehensive enough, the generalization is not strong, and the recognition accuracy is generally low. With the rapid development of deep learning technology [11], many researchers have applied it to SEI or signal recognition. For example, Ref. [12] applied the Hilbert-Huang transform to the received signal and converted the Hilbert spectrum into a grayscale image. Then, residual networks were used to learn the visual differences reflected in the Hilbert spectrum images.