Radio frequency fingerprinting feature fusion based on multi-domain discriminant kernel canonical correlation analysis

Abstract

Specific emitter identification (SEI) refers to the precise identification of a specific transmitter through the extraction of hardware fingerprint information from the received signal. The majority of related research focuses on the definition and extraction of new fingerprint features, with less emphasis on the comprehensive use of existing features. Because the characteristics of different analysis domains are complementary to the description of the radio frequency fingerprint, this paper proposes a multi-domain feature fusion strategy for SEI based on multi-domain discriminant kernel canonical correlation analysis (MDKCCA), which fully exploits the complementarity between the features of different domains and combines feature tag information. MDKCCA affords multi-domain feature dimensionality reduction and fusion in a high-dimensional space.The algorithm's performance is validated on four different types of real-world data sets using eight common fingerprint features in four feature analysis domains. The results show that this method eliminates the need for manual feature optimization and can significantly reduce the dimensionality of fusion features. The recognition rate on various targets exceeds 95%, which is higher than the best single feature. It is also superior to the simple feature synthesis method based on direct cascade or PCA dimensionality reduction transformation, the neural network-based feature synthesis method, and the feature fusion method based on discriminant canonical correlation methods.