Unintentional modulation microstructure enlargement

doi:10.23919/JSEE.2022.000052

Abstract

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.

Key words: radio frequency fingerprinting (RFF), unintentional modulation (UIM), adaptive signal decomposition (ASD), variational mode decomposition (VMD), similarity measurement

Liting SUN, Xiang WANG, Zhitao HUANG. Unintentional modulation microstructure enlargement[J]. Journal of Systems Engineering and Electronics, 2022, 33(3): 522-533.

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Parameter	Value
Radio frequency/MHz	1090
Sample frequency/MHz	250
Intermediate frequency/MHz	60
Samplesper/transmitter	$ \geqslant $ 300
Transmitter ID	R01 to R20

Process	Time
Auto-correlation	0.012
Cross-correlation	0.013
VMD	0.210
Original feature	0.073

$ f $	R2	R3	R4	R5
R1	0.972	?0.881	0.959	0.940
R2	?	?0.879	0.942	0.910
R3	?	?	?0.727	?0.864
R4	?	?	?	0.869

$ f $	R2	R3	R4	R5
R1	0.545	?0.316	0.921	0.763
R2	?	0.601	0.42	0.919
R3	?	?	?0.344	0.316
R4	?	?	?	0.587

$ f $	R2	R3	R4	R5
R1	0.177	1.008	0.197	0.234
R2	?	0.879	0.284	0.184
R3	?	?	1.047	0.844
R4	?	?	?	0.366