Abstract
Specific Emitter Identification (SEI) is a technique of extracting the radio frequency fingerprints of a received electromagnetic signal by only using external feature measurements to determine the specific emitter that transmits the signal. In recent years, the related theories and practical applications of SEI have been continuously improved, and the research on Radio Frequency Fingerprinting (RFF) feature extraction methods has made great progress. Based on domestic and foreign academic achievements, this paper systematically reviews the status quo of the fingerprint feature extraction method of SEI. In addition, a new feature classification framework is proposed based on the inherent logic of fingerprint feature extraction. The classification framework combines the description characteristics of different RFF features and the correlation between them. It divides the existing radio frequency features into two main categories, namely, direct measurement features and dimensionality reduction transform features, which have three levels. Finally, several potential research directions of fingerprint feature extraction are analyzed and explored, aiming to benefit the research and application of SEI.
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