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International Conference on Machine Learning and Intelligent Communications

MLICOM 2017: Machine Learning and Intelligent Communications pp 195–202Cite as

Radio Frequency Fingerprint Identification Method in Wireless Communication

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Abstract

The Radio frequency fingerprinting (RFF) generation mechanism is analyzed in this paper. It is proved to be a secure means for network security access. At the same time, the method of RFF extraction is also given. The characteristics of RFF are analyzed theoretically. Then, a high-precision fingerprint feature identification method based on Kalman filter is proposed. The results of the experiments show that the proposed system can work effectively in the environment where the signal-to-noise ratio (SNR) is higher than 10 dB, and the achieved identification rate is higher than 90%.

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References

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Author information

Authors and Affiliations

  1. Research and Development Center, China Academy of Launch Vehicle Technology, Beijing, China

    Zhe Li, Yanxin Yin & Lili Wu

Authors
  1. Zhe Li
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  2. Yanxin Yin
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  3. Lili Wu
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Corresponding author

Correspondence to Zhe Li .

Editor information

Editors and Affiliations

  1. Harbin Institute of Technology, Harbin, Heilongjiang, China

    Xuemai Gu

  2. Harbin Institute of Technology, Weihai, Heilongjiang, China

    Gongliang Liu

  3. Shandong University, Weihai, Heilongjiang, China

    Bo Li

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© 2018 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Cite this paper

Li, Z., Yin, Y., Wu, L. (2018). Radio Frequency Fingerprint Identification Method in Wireless Communication. In: Gu, X., Liu, G., Li, B. (eds) Machine Learning and Intelligent Communications. MLICOM 2017. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 226. Springer, Cham. https://doi.org/10.1007/978-3-319-73564-1_19

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  • DOI: https://doi.org/10.1007/978-3-319-73564-1_19

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-73563-4

  • Online ISBN: 978-3-319-73564-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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