Radar specific emitter identification based on open-selective kernel residual network

Abstract

Radar specific emitter identification (SEI) plays important roles in spectrum resource management and Internet of Things (IoT) security. This term refers to the identification of different individual devices of the same type by analyzing the unintentional modulation features caused by hardware imperfections (i.e., fingerprint features). Increasingly many researchers are using deep learning (DL)-based methods for SEI. However, DL-based methods still have many problems, including model generalization, recognition efficiency, and adaptability in open electromagnetic environments. This paper proposes a one-dimensional convolutional neural network (1D CNN)—called a selective kernel residual network (SKResNet)—capable of autonomously adjusting the receptive field, to improve the feature extraction capability. The transient part is cut efficiently from a radar signal sequence as the input of SKResNet using a universal preprocessing method. In addition, considering that most existing methods obtain incorrect results while identifying new emitters without any prior knowledge, SKResNet was improved using OpenMax—an open-set recognition method—to develop Open-SKResNet. As this network could identify unknown emitters effectively, generating reliable classification results of known emitters simultaneously, it could be of great importance and have broad application prospects.