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Tytuł artykułu

Specific emitter identification based on one-dimensional complex-valued residual networks with an attention mechanism

Tytuł czasopisma

Bulletin of the Polish Academy of Sciences Technical Sciences

Rocznik

2021

Wolumin

69

Numer

5

Autorzy

Qu, Lingzhi ; Yang, Junan ; Huang, Keju ; Liu, Hui

Afiliacje

Qu, Lingzhi : College of Electronic Engineering, National University of Defense Technology, Hefei, Anhui 230037, People’s Republic of China ; Yang, Junan : College of Electronic Engineering, National University of Defense Technology, Hefei, Anhui 230037, People’s Republic of China ; Huang, Keju : College of Electronic Engineering, National University of Defense Technology, Hefei, Anhui 230037, People’s Republic of China ; Liu, Hui : College of Electronic Engineering, National University of Defense Technology, Hefei, Anhui 230037, People’s Republic of China

Słowa kluczowe

complex-valued residual network ; specific emitter identification ; fingerprint characteristic ; attention mechanism ; one-dimensional convolution

Wydział PAN

Nauki Techniczne

Zakres

e138814

Bibliografia

  1.  K. Talbot, P. Duley, and M. Hyatt, “Specific emitter identification and verification,” Technol. Rev. J., vol.  Spring/Summer, pp. 113‒133, 2003.
  2.  G. Baldini, G. Steri, and R. Giuliani, “Identification of wireless devices from their physical layer radio-frequency fingerprints,” in Encyclopedia of Information Science and Technology, 4th Ed., 2018, ch. 533, pp. 6136‒6146.
  3.  G. Huang, Y. Yuan, X. Wang, and Z. Huang, “Specific emitter identification based on nonlinear dynamical characteristics,” Can. J. Electr. Comput. Eng., vol. 39, no. 1, pp. 34–41, 2016, doi: 10.1109/cjece.2015.2496143.
  4.  Y. Pan, H. Peng, T. Li, and W. Wang, “High-fidelity symbol synchronization for specific emitter identification,” in 2019 IEEE 3rd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC), 15‒17 March 2019, pp.  393‒398, doi: 10.1109/ITNEC.2019.8729181.
  5.  S. Ching-Sung and L. Chin-Teng, “A vector neural network for emitter identification,” IEEE Trans. Antennas Propag., vol. 50, no. 8, pp. 1120–1127, 2002, doi: 10.1109/TAP.2002.801387.
  6.  R. Klein, M.A. Temple, M.J. Mendenhall, and D.R. Reising, “Sensitivity Analysis of Burst Detection and RF Fingerprinting Classification Performance,” in 2009 IEEE International Conference on Communications, 14‒18 June 2009, pp. 1‒5, doi: 10.1109/ICC.2009.5199451.
  7.  O. Ureten and N. Serinken, “Bayesian detection of Wi-Fi transmitter RF fingerprints,” Electron. Lett., vol. 41, no. 6, pp.  373–374, 2005, doi: 10.1049/el:20057769.
  8.  J. Hall, M. Barbeau, and E. Kranakis, “Radio frequency fingerprinting for intrusion detection in wireless networks,” IEEE Trans. Dependable Secure Comput., vol. 12, pp. 1–35, 2005.
  9.  S. Guo, S. Akhtar, and A. Mella, “A Method for Radar Model Identification using Time-domain Transient Signals,” IEEE Transactions on Aerospace and Electronic Systems, pp. 1‒1, 2021, doi: 10.1109/TAES.2021.3074129.
  10.  Y. Yuan, Z. Huang, H. Wu, and X. Wang, “Specific emitter identification based on Hilbert–Huang transform-based time–frequency–energy distribution features,” IET Commun., vol. 8, no. 13, 2404–2412, 2014.
  11.  U. Satija, N. Trivedi, G. Biswal, and B. Ramkumar, “Specific emitter identification based on variational mode decomposition and spectral features in single hop and relaying scenarios,” IEEE Trans. Inf. Forensics Secur., vol. 14, no. 3, pp. 581–591, 2019, doi: 10.1109/ tifs.2018.2855665.
  12.  L. Li, H. B. Ji, and L. Jiang, “Quadratic time-frequency analysis and sequential recognition for specific emitter identification,” IET Signal Proc., vol. 5, pp. 568–574, 2011, doi: 10.1049/iet-spr.2010.0070.
  13.  J. Zhang, F. Wang, Z. Zhong, and O. Dobre, “Novel Hilbert Spectrum-Based Specific Emitter Identification for Single-Hop and Relaying Scenarios,” in 2015 IEEE Global Communications Conference (GLOBECOM), 6–10 Dec. 2015 2015, pp. 1–6, doi: 10.1109/ GLOCOM.2015.7417299.
  14.  Z. Tang and S. Li, “Steady Signal-Based Fractal Method of Specific Communications Emitter Sources Identification,” in Wireless Communications, Networking and Applications, Q.-A. Zeng, Ed., Springer India, New Delhi, 2016, pp. 809–819, doi: 10.1007/978-81- 322-2580-5_73.
  15.  F. Zhuo, Y. Huang, and J. Chen, “Radio frequency fingerprint extraction of radio emitter based on I/Q imbalance,” Procedia Comput. Sci., vol. 107, pp. 472–477, 2017, doi: 10.1016/j.procs.2017.03.092.
  16.  G. Huang, Y. Yuan, X. Wang, and Z. Huang, “Specific emitter identification based on nonlinear dynamical characteristics,” Can. J. Electr. Comput. Eng., vol. 39, pp. 34–41, 2016, doi: 10.1109/CJECE.2015.2496143.
  17.  M. Liu and J.F. Doherty, “Specific emitter identification using nonlinear device estimation,” in 2008 IEEE Sarnoff Symposium, 28‒30 April 2008, pp. 1–5, doi: 10.1109/SARNOF.2008.4520119.
  18.  M. Liu and J.F. Doherty, “Nonlinearity estimation for specific emitter identification in multipath channels,” IEEE Trans. Inf. Forensics Secur., vol. 6, no. 3, pp. 1076–1085, 2011, doi: 10.1109/TIFS.2011.2134848.
  19.  J. Dudczyk and A. Kawalec, “Specific emitter identification based on graphical representation of the distribution of radar signal parameters,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 63, no. 2, pp. 391–396, 2015, doi: 10.1515/bpasts-2015-0044.
  20.  Y. Zhao, L. Wu, J. Zhang, and Y. Li, “Specific emitter identification using geometric features of frequency drift curve,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 66, pp. 99–108, 2018, doi: 10.24425/119063.
  21.  Ł. Rybak and J. Dudczyk, “A geometrical divide of data particle in gravitational classification of moons and circles data sets,” Entropy, vol. 22, p. 16, 2020, doi: 10.3390/e22101088.
  22.  J. Han, T. Zhang, D. Ren, and X. Zheng, “Mechanism analysis and feature extraction algorithm of communication emitter fingerprint,” AEU Int. J. Electron. Commun., vol. 106, 2019, doi: 10.1016/j.aeue.2019.04.020.
  23.  M.K.M. Fadul, D.R. Reising, and M. Sartipi, “Identification of OFDM-based radios under rayleigh fading using RF-DNA and deep learning,” IEEE Access, vol. 9, pp. 17100–17113, 2021, doi: 10.1109/ACCESS.2021.3053491.
  24.  J. Dudczyk, “A method of feature selection in the aspect of specific identification of radar signals,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 65, pp. 113–119, 2017, doi: 10.1515/bpasts-2017-0014.
  25.  K. Sa, D. Lang, C. Wang, and Y. Bai, “Specific emitter identification techniques for the internet of things,” IEEE Access, vol. 8, pp. 1644– 1652, 2020, doi: 10.1109/ACCESS.2019.2962626.
  26.  Y. Pan, S. Yang, H. Peng, T. Li, and W. Wang, “Specific emitter identification based on deep residual networks,” IEEE Access, vol. 7, pp. 54425–54434, 2019, doi: 10.1109/ACCESS.2019.2913759.
  27.  L.J. Wong, W.C. Headley, S. Andrews, R.M. Gerdes, and A.J. Michaels, “Clustering learned CNN features from raw I/Q data for emitter identification,” in MILCOM 2018 – 2018 IEEE Military Communications Conference (MILCOM), 29–31 Oct. 2018, pp. 26–33, doi: 10.1109/MILCOM.2018.8599847.
  28.  M. Zhang, M. Diao, and L. Guo, “Convolutional neural networks for automatic cognitive radio waveform recognition,” IEEE Access, vol. 5, pp. 11074–11082, 2017, doi: 10.1109/ACCESS.2017.2716191.
  29.  N.E. West and T.O. Shea, “Deep architectures for modulation recognition,” in 2017 IEEE International Symposium on Dynamic Spectrum Access Networks (DySPAN), 6‒9 March 2017, pp.  1‒6, doi: 10.1109/DySPAN.2017.7920754.
  30.  Q. Wu et al., “Deep learning based RF fingerprinting for device identification and wireless security,” Electron. Lett., vol. 54, pp. 1405–1407, 2018, doi: 10.1049/el.2018.6404.
  31.  L. Ding, W. Shilian, F. Wang, and W. Zhang, “Specific emitter identification via convolutional neural networks,” IEEE Commun. Lett., vol. 22, no. 12, pp. 2591–2594, 2018, doi: 10.1109/LCOMM.2018.2871465.
  32.  G. Baldini, C. Gentile, R. Giuliani, and G. Steri, “A comparison of techniques for radiometric identification based on deep convolutional neural networks,” Electron. Lett., vol. 55, pp. 90–92, 2018, doi: 10.1049/el.2018.6229.
  33.  K. Huang, J. Yang, H. Liu, and P. Hu, “Deep adversarial neural network for specific emitter identification under varying frequency,” Bull. Pol. Acad. Sci. Tech. Sci., vol. 69, no. 2, p. e136716, 2021, doi: 10.24425/bpasts.2021.136737.
  34.  B. Wu, S. Yuan, P. Li, Z. Jing, S. Huang, and Y. Zhao, “Radar emitter signal recognition based on one-dimensional convolutional neural network with attention mechanism,” Sensors, vol. 20, p.  6350, 2020, doi: 10.3390/s20216350.
  35.  T.J. Shea and N. West, “Radio Machine Learning Dataset Generation with GNU Radio,” Proceedings of the GNU Radio Conference, vol 1, no.1, 2016. [Online]. Available: https://pubs.gnuradio.org/index.php/grcon/article/view/11.
  36.  T.J. O’Shea, T. Roy, and T.C. Clancy, “Over-the-Air deep learning based radio signal classification,” IEEE J. Sel. Top. Signal Process., vol. 12, no. 1, pp. 168‒179, 2018, doi: 10.1109/JSTSP.2018.2797022.
  37.  A. Hirose and S. Yoshida, “Generalization characteristics of complex-valued feedforward neural networks in relation to signal coherence,” IEEE Trans. Neural Networks, vol. 23, pp.  541–551, 2012, doi: 10.1109/TNNLS.2012.2183613.
  38.  C. Trabelsi et al., “Deep complex networks,” presented at the ICLR 2018, 2018.
  39.  Y. Ying and J. Li, “Radio frequency fingerprint identification based on deep complex residual network,” IEEE Access, vol. 8, pp. 204417– 204424, 2020, doi: 10.1109/ACCESS.2020.3037206.
  40.  S. Woo, J. Park, J.-Y. Lee, and I.S. Kweon, “CBAM: Convolutional Block Attention Module,” in Computer Vision – ECCV 2018, Cham, V. Ferrari, M. Hebert, C. Sminchisescu, and Y. Weiss, Eds., Springer International Publishing, 2018, pp. 3–19.

Data

15.09.2021

Typ

Article

Identyfikator

DOI: 10.24425/bpasts.2021.138814 ; ISSN 2300-1917
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