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Deep adversarial neural network for specific emitter identification under varying frequency

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Języki publikacji
EN
Abstrakty
EN
Specific emitter identification (SEI) is the process of identifying individual emitters by analyzing the radio frequency emissions, based on the fact that each device contains unique hardware imperfections. While the majority of previous research focuses on obtaining features that are discriminative, the reliability of the features is rarely considered. For example, since device characteristics of the same emitter vary when it is operating at different carrier frequencies, the performance of SEI approaches may degrade when the training data and the test data are collected from the same emitters with different frequencies. To improve performance of SEI under varying frequency, we propose an approach based on continuous wavelet transform (CWT) and domain adversarial neural network (DANN). The proposed approach exploits unlabeled test data in addition to labeled training data, in order to learn representations that are discriminative for individual emitters and invariant for varying frequencies. Experiments are conducted on received signals of five emitters under three carrier frequencies. The results demonstrate the superior performance of the proposed approach when the carrier frequencies of the training data and the test data differ.
Rocznik
Strony
art. no. e136737
Opis fizyczny
Bibliogr. 46 poz., rys., tab.
Twórcy
autor
  • College of Electronic Engineering, National University of Defense Technology, Hefei, Anhui 230037, China
autor
  • College of Electronic Engineering, National University of Defense Technology, Hefei, Anhui 230037, China
autor
  • College of Electronic Engineering, National University of Defense Technology, Hefei, Anhui 230037, China
autor
  • College of Electronic Engineering, National University of Defense Technology, Hefei, Anhui 230037, China
Bibliografia
  • [1] K.I. Talbot, P.R. Duley, and M.H. Hyatt, “Specific emitter identification and verification”, Technol. Rev. 2003, 113–133, (2003).
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  • [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).
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  • [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).
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  • [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”, n: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Honolulu, Hawaii, 2017, pp. 7167–7176.
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  • [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.
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2021).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-5d20b429-3b95-4f68-b35e-b296e02b1671
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