Open-set recognition of specific emitter based on complex-valued convolutional neural network

Abstract

Specific emitter identification (SEI) plays an important role in enhancing physical layer transmission security. However, with the promotion of wireless technology, the environment is filled with a large number of unknown wireless signals. SEI will face a more challenging scenario referred to as “open set.” To cope with the above difficulties, an open-set recognition (OSR) model based on complex-valued convolutional neural network (CVCNN) is proposed. The CVCNN can adapt to IQ signal input and extract complex domain features. Furthermore, a novel inter-class loss is proposed to effectively improve the classification performance. Finally, the classifier is designed based on the incremental approach. It can continuously learn new classes to achieve the recognition of multiple unknown emitters. The experiments show that compared with the real-valued convolutional neural network and the single loss function, the accuracy is improved by 3.8% and 10%, respectively.