Table of Contents Author Guidelines Submit a Manuscript
International Journal of Antennas and Propagation
Volume 2017, Article ID 1538728, 6 pages
https://doi.org/10.1155/2017/1538728
Research Article

Radio Frequency Fingerprint Extraction Based on Multidimension Permutation Entropy

College of Electronic Science and Engineering, National University of Defense Technology, No. 137, Street Yanwachi, Changsha, China

Correspondence should be addressed to Shouyun Deng; moc.621@gnednuyuohs

Received 8 February 2017; Revised 28 June 2017; Accepted 30 July 2017; Published 28 August 2017

Academic Editor: Symeon Nikolaou

Copyright © 2017 Shouyun Deng et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. O. Ureten and N. Serinken, “Wireless security through RF fingerprinting,” Canadian Journal of Electrical and Computer Engineering, vol. 32, no. 1, pp. 27–33, 2007. View at Publisher · View at Google Scholar · View at Scopus
  2. V. Lakafosis, A. Traille, H. Lee et al., “RF fingerprinting physical objects for anticounterfeiting applications,” IEEE Transactions on Microwave Theory and Techniques, vol. 59, no. 2, pp. 504–514, 2011. View at Publisher · View at Google Scholar
  3. W. C. Suski II, M. A. Temple, M. J. Mendenhall, and R. F. Mills, “Radio frequency fingerprinting commercial communication devices to enhance electronic security,” International Journal of Electronic Security and Digital Forensics, vol. 1, no. 3, pp. 301–322, 2008. View at Publisher · View at Google Scholar · View at Scopus
  4. S. Xu, L. Xu, Z. Xu, and B. Huang, “Individual radio transmitter identification based on spurious modulation characteristic of signal envelop,” in Proceedings of the IEEE Military Communications Conference, MILCOM '08, USA, 2008. View at Publisher · View at Google Scholar · View at Scopus
  5. J. Zhang, F. Wang, O. A. Dobre, and Z. Zhong, “Specific emitter identification via hilbert-huang transform in single-hop and relaying scenarios,” IEEE Transactions on Information Forensics and Security, vol. 11, no. 6, pp. 1192–1205, 2016. View at Publisher · View at Google Scholar · View at Scopus
  6. R. W. Klein, M. A. Temple, and M. J. Mendenhall, “Application of wavelet-based RF fingerprinting to enhance wireless network security,” Journal of Communications and Networks, vol. 11, no. 6, pp. 544–555, 2009. View at Publisher · View at Google Scholar
  7. S. C. Pires, P. M. Cabral, and J. C. Pedro, “Radio frequency carrier amplitude-burst transmitters - from architecture to circuit,” IET Microwaves, Antennas and Propagation, vol. 9, no. 3, pp. 271–280, 2015. View at Publisher · View at Google Scholar · View at Scopus
  8. H. W. Kang, Y. S. Cho, D. H. Youn et al., “On compensating nonlinear distortions of an OFDM system using an efficient adaptive predistorter,” IEEE Transactions on Communications, vol. 47, no. 4, pp. 522–526, 1999. View at Publisher · View at Google Scholar · View at Scopus
  9. T. J. Bihl, K. W. Bauer, and M. A. Temple, “Feature selection for RF fingerprinting with multiple discriminant analysis and using ZigBee device emissions,” IEEE Transactions on Information Forensics and Security, vol. 11, no. 8, pp. 1862–1874, 2016. View at Publisher · View at Google Scholar · View at Scopus
  10. C. Bandt and B. Pompe, “Permutation entropy: a natural complexity measure for time series,” Physical Review Letters, vol. 88, Article ID 174102, 2002. View at Publisher · View at Google Scholar
  11. K. El-Darymli, E. W. Gill, C. Moloney, P. McGuire, and D. Power, “Permutation entropy for signal analysis: A case study of synthetic aperture radar imagery,” in Proceedings of the 14th IEEE Canadian Workshop on Information Theory, CWIT 2015, pp. 66–70, can, July 2015. View at Publisher · View at Google Scholar · View at Scopus
  12. M. Hearst, S. Dumais, E. Osman, J. Platt, and B. Scholkopf, “Support vector machines,” IEEE Intelligent Systems and their Applications, vol. 13, no. 4, pp. 18–28, 1998. View at Publisher · View at Google Scholar
  13. D. Gao and T. Zhang, “Support vector machine classifiers using RBF kernels with clustering-based centers and widths,” in Proceedings of the 2007 International Joint Conference on Neural Networks, IJCNN 2007, pp. 2971–2976, usa, August 2007. View at Publisher · View at Google Scholar · View at Scopus