Abstract
Radio frequency fingerprinting (RFF) is a technology that identifies the specific emitter of a received electromagnetic signal by external measurement of the minuscule hardware-level, device-specific imperfections. The RFF-related information is mainly in the form of unintentional modulation (UIM), which is subtle enough to be effectively imperceptible and is submerged in the intentional modulation (IM). It is necessary to minimize the influence of the IM and expand the slight differences between emitters for successful RFF. This paper proposes a UIM microstructure enlargement (UMME) method based on feature-level adaptive signal decomposition (ASD), accompanied by autocorrelation and cross-correlation analysis. The common IM part is evaluated by analyzing a newly-defined benchmark feature. Three different indexes are used to quantify the similarity, distance, and dependency of the RFF features from different devices. Experiments are conducted based on the real-world signals transmitted from 20 of the same type of radar in the same working mode. The visual image qualitatively shows the magnification of feature differences; different indicators quantitatively describe the changes in features. Compared with the original RFF feature, recognition results based on the Gaussian mixture model (GMM) classifier further validate the effectiveness of the proposed algorithm.
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