A comparison study of radar emitter identification based on signal transients

Radio Frequency Fingerprint Collaborative Intelligent Identification Using Incremental Learning

“…S. Guo, C. Song et al proposed an empirical mode decomposition (EMD) method to extract features, but this method has modal aliasing [11], [12]. Y. Liu et al introduced a feature extraction method based on Hilbert Huang transform (HHT), which combines EMD algorithm with Hilbert transform to extract instantaneous frequency and amplitude.…”

Radio Frequency Fingerprint Collaborative Intelligent Blind Identification for Green Radios

“…Reference [7] proposed an identification method based on the Wegener-Well distribution, but this method had the disadvantage of the existence of cross-interference terms. Reference [8] proposed an individual identification method based on empirical mode decomposition, which unfortunately suffered from modal aliasing. Zhu et al extracted the fractal dimension, pulse rise, fall time, kurtosis and other characteristics to identify RFFI, but this method was sensitive to noise [9].…”

A Nonlinear Fingerprint-Level Radar Simulation Modeling Method for Specific Emitter Identification

“…Then, the fingerprint feature is extracted to obtain the fine features containing the individual information of the emitter; finally, compared with the database, the specific emitter of the signal is determined by the classification and recognition algorithm, and the individual emitter is identified. In recent years, the theory and practical application of emitter individual identification technology are constantly improved, and the research of fingerprint feature extraction method has made great progress [4][5][6][7][8][9][10][11][12][13][14][15][16][17].…”