# Open-Set Specific Emitter Identification Based on Prototypical Networks and Extreme Value Theory
## 1 Introduction
[26; 27] used OpenMax [28] based on the extreme value theory (EVT) to recognize the communication emitters and high-resolution range profiles of radar, achieving performance advantages compared with the traditional open-set recognition model. In order to enhance the discriminative power of deeply learned features, [PERSON]. [29] proposed center loss for face recognition tasks. The center loss simultaneously learned a center for deep features of each class and penalized the distances between the deep features and their corresponding class centers. It significantly improved upon previous results and is expected to be used for open-set recognition. [PERSON]. [30] proposed an unsupervised class-distance learning method, which used an auxiliary dataset containing only open classes to learn the decision boundary between closed and open sets and achieved improved results. In recent years, prototypical networks combining prototype learning ideas and deep neural networks have been used for image recognition under few-shot conditions [31], class-incremental learning [32], and open-set recognition [33]. These methods have achieved better recognition performance compared with traditional CNNs. Prototypical networks have obvious advantages in solving few-shot problems and improving generalization performance. Additionally, the classification principle of prototypical networks is more favorable for open-set recognition. Based on this, we propose an open-set SEI model for one-dimensional sequence signals by combining prototypical networks and EVT. We creatively introduced an attention mechanism and ShuffleNet into a one-dimensional neural network and combined this prototype network and extreme value theory for the first time to obtain a higher recognition performance and robustness. The results of the experiments on the dataset collected by 5 ZigBee devices and 10 USRP 310 devices showed that the proposed model had a higher recognition accuracy than other models such as OpenMax. The recognition accuracy of the five ZigBee devices reached 95% at 0 dB, and it reached over 90% at a mixed SNR of \(-\)4-10 dB for the 10 USRP 310 devices. The contributions of the proposed model are as follows:  
1. A one-dimensional CNN integrating an attention mechanism was designed. Meanwhile, group convolution and channel shuffle were introduced into the network, which reduced the complexity and overfitting and effectively improved the recognition performance. 2. Prototype learning was combined with the one-dimensional CNN. Distance-based cross-entropy loss and prototype loss were used to train the network to complete the separation of inter-class signals and the aggregation of intra-class signals in the feature space. 3. Combining EVT, Weibull models were fitted for each known class based on the distance from the sample features to the mean features. The open-set recognition was completed based on the Weibull models and the distance between the features of the test samples and the mean features of known classes. The rest of the paper is organized as follows. Section 2 introduces the classification principle of the prototypical networks and the basic theory for applying EVT to open-set recognition. Section 3 presents the design of the open-set recognition model, including the network structure, classification algorithm, and loss function. Simulation experiments on five ZigBee devices and an analysis of the simulation results are provided in Section 4. Finally, a brief conclusion is presented in Section 5.