Radio Frequency Fingerprint Recognition Based on Kalman Filtering and Random Matrix Theory

Zhang, Y; Xiong, J; Liu, B; Huang, T

Radio Frequency Fingerprint Recognition Based on Kalman Filtering and Random Matrix Theory

Zhang, Y Xiong, J Liu, B

Huang, T

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Conference Proceeding
Citation:: IEEE International Symposium on Broadband Multimedia Systems and Broadcasting Bmsb, 2025, 00, pp. 1-6
Issue Date:: 2025-01-01

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Field	Value	Language
dc.contributor.author	Zhang, Y
dc.contributor.author	Xiong, J
dc.contributor.author	Liu, B https://orcid.org/0000-0002-3603-6617
dc.contributor.author	Huang, T
dc.date	2025-06-11
dc.date.accessioned	2025-10-23T06:01:21Z
dc.date.available	2025-10-23T06:01:21Z
dc.date.issued	2025-01-01
dc.identifier.citation	IEEE International Symposium on Broadband Multimedia Systems and Broadcasting Bmsb, 2025, 00, pp. 1-6
dc.identifier.issn	2155-5044
dc.identifier.issn	2155-5052
dc.identifier.uri	http://hdl.handle.net/10453/190549
dc.description.abstract	Radio frequency fingerprint (RFF) technology is instrumental in achieving secure and dependable device identification by exploiting the unique imperfections inherent in hardware. Nonetheless, factors such as interference from noise, the complexities of multipath propagation, and fluctuations in environmental conditions can severely compromise the accuracy of RFF recognition. In this study, we introduce an innovative RFF recognition approach that melds Kalman filtering with random matrix theory (RMT) to enhance recognition accuracy. The Kalman filter is utilized to refine the raw I/Q data, mitigating noise and elevating signal integrity. Concurrently, RMT harnesses the eigenvalue distributions to discern the global characteristics of the signals. These refined features are subsequently input into a deep learning classifier to facilitate device identification. Our experimental findings, based on a substantial dataset, ascertain that the proposed method significantly surpasses conventional techniques in terms of recognition accuracy and robustness.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE International Symposium on Broadband Multimedia Systems and Broadcasting Bmsb
dc.relation.ispartof	2025 IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)
dc.relation.isbasedon	10.1109/BMSB65076.2025.11165736
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Radio Frequency Fingerprint Recognition Based on Kalman Filtering and Random Matrix Theory
dc.type	Conference Proceeding
utslib.citation.volume	00
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	University of Technology Sydney/Faculty of Engineering and Information Technology/School of Computer Science
pubs.organisational-group	University of Technology Sydney/UTS Groups
pubs.organisational-group	University of Technology Sydney/UTS Groups/Australian Artificial Intelligence Institute (AAII)
pubs.organisational-group	University of Technology Sydney/UTS Groups/The Trustworthy Digital Society
utslib.copyright.status	open_access	*
dc.date.updated	2025-10-23T06:01:19Z
pubs.finish-date	2025-06-13
pubs.publication-status	Published
pubs.start-date	2025-06-11
pubs.volume	00

Abstract:

Radio frequency fingerprint (RFF) technology is instrumental in achieving secure and dependable device identification by exploiting the unique imperfections inherent in hardware. Nonetheless, factors such as interference from noise, the complexities of multipath propagation, and fluctuations in environmental conditions can severely compromise the accuracy of RFF recognition. In this study, we introduce an innovative RFF recognition approach that melds Kalman filtering with random matrix theory (RMT) to enhance recognition accuracy. The Kalman filter is utilized to refine the raw I/Q data, mitigating noise and elevating signal integrity. Concurrently, RMT harnesses the eigenvalue distributions to discern the global characteristics of the signals. These refined features are subsequently input into a deep learning classifier to facilitate device identification. Our experimental findings, based on a substantial dataset, ascertain that the proposed method significantly surpasses conventional techniques in terms of recognition accuracy and robustness.

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http://hdl.handle.net/10453/190549