A Method for Specific Emitter Identification Based on Polarimetric Domain Feature Learning and Extraction

Specific Emitter Identification (SEI) distinguishes individual emitters by extracting subtle features from intercepted radio frequency signals. This process relies on the design and extraction of specific features. Current methods for selecting and characterizing radio frequency fingerprints vary by individual, and the extraction process is closely coupled with environmental conditions. As a result, the generality of such identification algorithms is often limited, particularly when the application environment does not match the premise of feature design, leading to rapid degradation or even failure of individual identification performance. This paper proposes a deep clustering model based on polarization feature learning for identifying individual communication emitters. The approach involves constructing a guided network to extract datasets of polarization features from communication signals and utilizing a contrastive representation learning network to extract dual-polarization features from I/Q data samples. Subsequently, a Bayesian nonparametric (BNP) class mixture model algorithm, capable of inferring an unknown number of clusters, is employed to build a multi-level clustering network for clustering analysis of the extracted features. Under 5 dB conditions, the method described in this paper achieves an average recognition accuracy of 87.5%.