Details
Title
Deep adversarial neural network for specific emitter identification under varying frequencyJournal title
Bulletin of the Polish Academy of Sciences Technical SciencesYearbook
2021Volume
69Issue
2Affiliation
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, ChinaAuthors
Keywords
specific emitter identification ; unsupervised domain adaptation ; transfer learning ; deep learningDivisions of PAS
Nauki TechniczneCoverage
e136737Bibliography
- K.I. Talbot, P.R. Duley, and M.H. Hyatt, “Specific emitter identification and verification”, Technol. Rev. 2003, 113–133, (2003).
- 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.
- A.E. Spezio, “Electronic warfare systems”, IEEE Trans. Microw. Theory Tech. 50(3), 633–644 (2002).
- O. Ureten and N. Serinken, “Wireless security through rf fingerprinting”, Can. J. Electr. Comp. Eng. 32(1), 27–33 (2007).
- 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).
- 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.
- Y. Huang, et al., “Radio frequency fingerprint extraction of radio emitter based on i/q imbalance”, Procedia Computer Science 107, 472–477 (2017).
- 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).
- 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).
- 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).
- J. Lundén and V. Koivunen, “Automatic radar waveform recognition”, IEEE J. Sel. Top. Signal Process. 1(1), 124–136 (2007).
- 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).
- 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).
- 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.
- 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).
- 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.
- 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).
- Y. Jia, S. Zhu, and L. Gan, “Specific emitter identification based on the natural measure”, Entropy 19(3), 117 (2017).
- 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).
- 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).
- 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).
- Q. Wu, et al., “Deep learning based rf fingerprinting for device identification and wireless security”, Electron. Lett. 54(24), 1405–1407 (2018).
- L. Ding, S. Wang, F. Wang, and W. Zhang, “Specific emitter identification via convolutional neural networks”, IEEE Commun. Lett. 22(12), 2591–2594 (2018).
- 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).
- 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).
- 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.
- 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.
- 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).
- 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).
- 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.
- 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).
- 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.
- 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.
- M. Wang and W. Deng, “Deep visual domain adaptation: A survey”, Neurocomputing 312, 135–153 (2018). doi: 10.1016/j. neucom.2018.05.083.
- 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.
- Y. Ganin, et al., “Domain-adversarial training of neural networks”, J. Mach. Learn. Res. 17(1), 2096–2030 (2016).
- 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.
- I. Goodfellow, et al., “Generative adversarial nets”, in: Advances in Neural Information Processing Systems, Montreal, Canada, 2014, pp. 2672–2680.
- 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).
- 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.
- 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.
- L. Maaten and G. Hinton, “Visualizing data using t-sne”, J. Mach. Learn. Res. 9, 2579–2605 (2008).
- 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.
- 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.
- 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.
- 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.