Specific Emitter Identification Based on Feature Diagram Superposition

Specific emitter identification (SEI) refers to individual emitter identification by analyzing hardware defect characteristics carried by received signals. Because distortion characteristics of components inside transmitter are complex and their differences are subtle, it is difficult to extract a single Radio frequency fingerprint (RFF) feature to improve recognition performance. Therefore, this paper proposes a novel SEI method based on feature diagram superposition. By constructing emitter distortion model and analyzing visual representation of different types of distortion, superimposed feature diagram can better reflect fingerprint features of different emitters compared to a single feature. Combining feature diagram superposition with neural network model, experimental simulation shows that the proposed method can enhance performance of individual emitter identification without increasing model complexity.