Details

Title

Deep adversarial neural network for specific emitter identification under varying frequency

Journal title

Bulletin of the Polish Academy of Sciences Technical Sciences

Yearbook

2021

Volume

69

Issue

2

Authors

Affiliation

Huang, Keju : College of Electronic Engineering, National University of Defense Technology, Hefei, Anhui 230037, China ; Yang, Junan : College of Electronic Engineering, National University of Defense Technology, Hefei, Anhui 230037, China ; Liu, Hui : College of Electronic Engineering, National University of Defense Technology, Hefei, Anhui 230037, China ; Hu, Pengjiang : College of Electronic Engineering, National University of Defense Technology, Hefei, Anhui 230037, China

Keywords

specific emitter identification ; unsupervised domain adaptation ; transfer learning ; deep learning

Divisions of PAS

Nauki Techniczne

Coverage

e136737

Bibliography

  1. K.I. Talbot, P.R. Duley, and M.H. Hyatt, “Specific emitter identification and verification”, Technol. Rev. 2003, 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, pp. 6136–6146, 4th Edition, IGI Global, 2018.
  3. A.E. Spezio, “Electronic warfare systems”, IEEE Trans. Microw. Theory Tech. 50(3), 633–644 (2002).
  4. O. Ureten and N. Serinken, “Wireless security through rf fingerprinting”, Can. J. Electr. Comp. Eng. 32(1), 27–33 (2007).
  5. S.U. Rehman, K.W. Sowerby, and C. Coghill, “Radio-frequency fingerprinting for mitigating primary user emulation attack in low-end cognitive radios”, IET Commun. 8(8), 1274–1284 (2014).
  6. V. Brik, S. Banerjee, M. Gruteser, and S. Oh, “Wireless device identification with radiometric signatures”, in: Proceedings of the 14th ACM international Conference on Mobile Computing and Networking, San Francisco, USA: ACM, 2008, pp. 116– 127.
  7. Y. Huang, et al., “Radio frequency fingerprint extraction of radio emitter based on i/q imbalance”, Procedia Computer Science 107, 472–477 (2017).
  8. L.J. Wong, W.C. Headley, and A.J. Michaels, “Specific emitter identification using convolutional neural network-based iq imbalance estimators”, IEEE Access 7, 33544–33555 (2019).
  9. G. López-Risueño, J. Grajal, and A. Sanz-Osorio, “Digital channelized receiver based on time-frequency analysis for signal interception”, IEEE Trans. Aerosp. Electron. Syst. 41(3), 879–898 (2005).
  10. C. Bertoncini, K. Rudd, B. Nousain, and M. Hinders, “Wavelet fingerprinting of radio-frequency identification (rfid) tags”, EEE Trans. Ind. Electron. 59(12), 4843–4850 (2011).
  11. J. Lundén and V. Koivunen, “Automatic radar waveform recognition”, IEEE J. Sel. Top. Signal Process. 1(1), 124–136 (2007).
  12. L. Li, H.B. Ji, and L. Jiang, “Quadratic time–frequency analysis and sequential recognition for specific emitter identification”, IET Signal Process. 5(6), 568–574 (2011).
  13. 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. 8(13), 2404–2412 (2014).
  14. 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), San Diego, USA, IEEE, 2015, pp. 1–6.
  15. J. Zhang, F. Wang, O. Dobre, and Z. Zhong, “Specific emitter identification via Hilbert–Huang transform in single-hop and relaying scenarios”, IEEE Trans. Inf. Forensic Secur. 11(6), 1192–1205 (2016).
  16. Z. Tang and S. Li, “Steady signal-based fractal method of specific communications emitter sources identification”, in: Wireless Communications, Networking and Applications, pp. 809– 819, Springer, 2016.
  17. G. Huang, Y. Yuan, X. Wang, and Z. Huang, “Specific emitter identification based on nonlinear dynamical characteristics”, Can. J. Electr. Comp. Eng. 39(1), 34–41 (2016).
  18. Y. Jia, S. Zhu, and L. Gan, “Specific emitter identification based on the natural measure”, Entropy 19(3), 117 (2017).
  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. 63(2), 391–396 (2015).
  20. Y. Zhao, Y. Li, L. Wui, and J. Zhang, “Specific emitter identification using geometric features of frequency drift curve”, Bull. Pol. Acad. Sci. Tech. Sci. 66(1), 99–108 (2018).
  21. L. Rybak and J. Dudczyk, “A geometrical divide of data particle in gravitational classification of moons and circles data sets”, Entropy 22(10), 1088 (2020).
  22. Q. Wu, et al., “Deep learning based rf fingerprinting for device identification and wireless security”, Electron. Lett. 54(24), 1405–1407 (2018).
  23. L. Ding, S. Wang, F. Wang, and W. Zhang, “Specific emitter identification via convolutional neural networks”, IEEE Commun. Lett. 22(12), 2591–2594 (2018).
  24. K. Merchant, S. Revay, G. Stantchev, and B. Nousain, “Deep learning for rf device fingerprinting in cognitive communication networks”, IEEE J. Sel. Top. Signal Process. 12(1), 160–167 (2018).
  25. Y. Pan, S. Yang, H. Peng, T. Li, and W. Wang, “Specific emitter identification based on deep residual networks”, IEEE Access 7, 54425– 54434 (2019).
  26. J. Matuszewski and D. Pietrow, “Recognition of electromagnetic sources with the use of deep neural networks”, in XII Conference on Reconnaissance and Electronic Warfare Systems, 2019, vol. 11055, pp. 100–114, doi: 10.1117/12.2524536.
  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 emitteridentification”, in: MILCOM 2018-2018 IEEE Military Communications Conference (MILCOM), Los Angeles, USA, 2018, pp. 26–33.
  28. G. Baldini, C. Gentile, R. Giuliani, and G. Steri, “Comparison of techniques for radiometric identification based on deep convolutional neural networks”, Electron. Lett. 55(2), 90–92 (2018).
  29. W. Wang, Z. Sun, S. Piao, B. Zhu, and K. Ren, “Wireless physical-layer identification: Modeling and validation”, IEEE Trans. Inf. Forensic Secur. 11(9), 2091–2106 (2016).
  30. S. Andrews, R.M. Gerdes, and M. Li, “Towards physical layer identification of cognitive radio devices”, IEEE Conference on Communications and Network Security (CNS), Las Vegas, USA, IEEE, 2017, pp. 1–9.
  31. I.F. Akyildiz, W.Y. Lee, M.C. Vuran, and S. Mohanty, “Next generation/dynamic spectrum access/cognitive radio wireless networks: A survey”, Comput. Netw. 50(13), 2127–2159 (2006).
  32. S.J. Pan and Q. Yang, “A survey on transfer learning”, IEEE Trans. Knowl. Data Eng. 22(10), 1345–1359 (2009), doi: 10.1109/ TKDE.2009.191.
  33. Y. Sharaf-Dabbagh and W. Saad, “Transfer learning for device fingerprinting with application to cognitive radio networks”, in: 2015 IEEE 26th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC), Hong Kong, China, 2015, pp. 2138–2142.
  34. M. Wang and W. Deng, “Deep visual domain adaptation: A survey”, Neurocomputing 312, 135–153 (2018). doi: 10.1016/j. neucom.2018.05.083.
  35. Y. Ganin and V. Lempitsky, “Unsupervised domain adaptation by backpropagation”, in: Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, Lille, France, 2015, pp. 1180–1189.
  36. Y. Ganin, et al., “Domain-adversarial training of neural networks”, J. Mach. Learn. Res. 17(1), 2096–2030 (2016).
  37. G. Wilson and D.J. Cook, “A survey of unsupervised deep domain adaptation”, CoRR, 2018, abs/1812.02849. Available from: http://arxiv. org/abs/1812.02849.
  38. I. Goodfellow, et al., “Generative adversarial nets”, in: Advances in Neural Information Processing Systems, Montreal, Canada, 2014, pp. 2672–2680.
  39. 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. Forensic Secur. 14(3), 581–591 (2018).
  40. E. Tzeng, J. Hoffman, K. Saenko, and T. Darrell, “Adversarial discriminative domain adaptation”, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, Hawaii, 2017, pp. 7167–7176.
  41. K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition”, in: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, USA, 2016, pp. 770–778.
  42. L. Maaten and G. Hinton, “Visualizing data using t-sne”, J. Mach. Learn. Res. 9, 2579–2605 (2008).
  43. C. Chen, et al., “Progressive feature alignment for unsupervised domain adaptation”, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2019, pp. 627–636.
  44. P. Panareda-Busto and J. Gall, “Open set domain adaptation”, in: Proceedings of the IEEE International Conference on Computer Vision (ICCV), Venice, Italy, 2017, pp. 754–763.
  45. Z. Cao, M. Long, J. Wang, and M.I. Jordan, “Partial transfer learning with selective adversarial networks”, in: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Salt Lake City, USA, 2018, pp. 2724–2732.
  46. K. You, M. Long, Z. Cao, J. Wang, and M.I. Jordan, “Universal domain adaptation”, in: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Long Beach, USA,2019.

Date

09.03.2021

Type

Article

Identifier

DOI: 10.24425/bpasts.2021.136737

Source

Bulletin of the Polish Academy of Sciences: Technical Sciences; 2021; 69; 2; e136737
×