Specific emitter identification based on Signal-Graph Capsule Network (SGCN)

Abstract

As a typical pattern recognition problem, specific emitter identification (SEI) is a crucial step to achieve efficient spectrum sensing. In this work, an emitter identification method based on Signal Graph Capsule Network, which refered as SGCN, is proposed. First, emitter signal is transformed into an undirected graph according to the Euclidean distance from its sampling point, and then take the undirected graph as the input of the network. Second, optimizing the topological structural characteristics by graph convolution operation on this undirected graph. Finally, by introduce the capsule network to improve the generalization ability and enhance the robustness. Extensive analysis and experiments on 30 individual emitters signals demonstrates the attentiveness of the proposed model.