Unsupervised feature extraction for specific emitter identification

Imperfections in the analog components of emitters cause distortion in the transmitted signals. These distortions serve as unique fingerprints for the purpose of Specific Emitter Identification (SEI). Features for SEI are typically extracted using either a distortion-models based method or a machine learning based method. In this study, we have integrated these two approaches for feature extraction from emitters. By incorporating specialist knowledge, the distortion model, into the neural networks, we ensure the interpretability of the extracted features and enhance identification accuracy. We propose a set of concatenated networks designed to extract parameters related to In-phase/Quadrature imbalance and phase noise models of the emitter. Our simulation results demonstrate that this scheme outperforms both the conventional distortion-models-based and the machine learning-based methods in terms of feature extraction performance.