Unintentional modulation microstructure enlargement

doi:10.23919/JSEE.2022.000052

Journal of Systems Engineering and Electronics ›› 2022, Vol. 33 ›› Issue (3): 522-533.doi: 10.23919/JSEE.2022.000052

收稿日期:2021-02-04 出版日期:2022-06-18 发布日期:2022-06-24

Unintentional modulation microstructure enlargement

Liting SUN, Xiang WANG*(), Zhitao HUANG()

¹ Department of Electronic Science, National University of Defense Technology, Changsha 410073, China

Received:2021-02-04 Online:2022-06-18 Published:2022-06-24
Contact: Xiang WANG E-mail:christopherwx@163.com;huangzhitao@nudt.edu.cn
About author:|SUN Liting was born in 1994. She received her B.E. degree from the School of Information Science and Engineering, Shandong University, Ji ’nan, China, in 2017. She is currently pursuing her Ph.D. degree with State Key Laboratory of Complex Electromagnetic Environment Effects on Electronics and Information System, National University of Defense Technology, Changsha, China. Her current research interests include cognitive radio, signal processing, and physical-layer security. E-mail: slt2009@yeah.net||WANG Xiangwas born in 1985. He received his B.S. and Ph.D. degrees in electronic science and engineering from the National University of Defense Technology, Changsha, Hunan, China, in 2007 and 2013, respectively, where he is currently an associate professor with the College of Electronic Science and Engineering. His research interests include blind asignal processing in radar and communication applications and pattern recognition. E-mail: christopherwx@163.com||HUANG Zhitao was born in 1976. He received his B.S. and Ph.D. degrees in information and communication engineering from the National University of Defense Technology, Changsha, Hunan, China, in 1998 and 2003, respectively, where he is currently a professor with the College of Electronic Science and Engineering. His research interests include radar and communication signal processing and array signal processing. E-mail: huangzhitao@nudt.edu.cn
Supported by:
This work was supported by the Program for Innovative Research Groups of the Hunan Provincial Natural Science Foundation of China (2019JJ10004).

摘要/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

. [J]. Journal of Systems Engineering and Electronics, 2022, 33(3): 522-533.

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

图/表 21

参考文献 38

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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

Unintentional modulation microstructure enlargement

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$ f $	R2	R3	R4	R5
R1	0.694	2.446	0.340	0.470
R2	?	1.778	0.861	0.295
R3	?	?	2.545	2.013
R4	?	?	?	0.701

$ f $	R2	R3	R4	R5
R1	0.852	0.815	0.867	0.805
R2	?	0.804	0.860	0.814
R3	?	?	0.822	0.791
R4	?	?	?	0.825

$ f $	R2	R3	R4	R5
R1	0.724	0.706	0.708	0.733
R2	?	0.744	0.731	0.778
R3	?	?	0.720	0.739
R4	?	?	?	0.740

$\tilde f$	PE			ED			MI
$\tilde f$	X	Y0	Y	X	Y0	Y	X	Y0	Y
I=3	0.958	0.678	0.609	0.220	0.266	0.632	0.86	0.861	0.740
I=5	0.894	0.539	0.573	0.522	0.524	1.214	0.826	0.842	0.732
I=10	0.745	0.530	0.567	0.537	0.550	1.235	0.820	0.833	0.735
I=15	0.637	0.543	0.568	0.598	0.600	1.396	0.817	0.841	0.732
I=20	0.641	0.546	0.577	0.583	0.590	1.349	0.817	0.836	0.733

Benchmark feature	4 GMM		6 GMM
Benchmark feature	I=5	I=10	I=5	I=10
R1	90.640	67.378	92.836	74.132
R4	91.728	69.652	93.704	75.998
$\hat Y(k)$	93.248	70.762	94.992	76.678