Details

PDF BIBTEX RIS

Title

Accurate identification on individual similar communication emitters by using HVG-NTE feature

Journal title

Bulletin of the Polish Academy of Sciences Technical Sciences

Yearbook

2021

Volume

69

Issue

2

Affiliation

Li, Ke : School of Information and Computer, Anhui Agricultural University, Hefei, Anhui, 230036, China ; Li, Ke : Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei, Anhui, 230036, China ; Li, Ke : Key Laboratory of Specialty Fiber Optics and Optical Access Networks, Shanghai University, Shanghai, 200072, China ; Ge, Wei : School of Information and Computer, Anhui Agricultural University, Hefei, Anhui, 230036, China ; Ge, Wei : Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei, Anhui, 230036, China ; Yang, Xiaoya : School of Information and Computer, Anhui Agricultural University, Hefei, Anhui, 230036, China ; Yang, Xiaoya : Anhui Provincial Engineering Laboratory for Beidou Precision Agriculture Information, Anhui Agricultural University, Hefei, Anhui, 230036, China ; Xu, Zhengrong : School of Information and Computer, Anhui Agricultural University, Hefei, Anhui, 230036, China

Authors

Li, Ke ; Ge, Wei ; Yang, Xiaoya ; Xu, Zhengrong

Keywords

communication emitter ; identification ; feature extraction ; HVG ; NTE

Divisions of PAS

Nauki Techniczne

Coverage

e136741

Bibliography

  1.  J. Dudczyk, “Radar emission sources identification based on hierarchical agglomerative clustering for large data sets”, J. Sens. 2016, 1879327 (2016).
  2.  G. Manish, G. Hareesh, and M. Arvind, “Electronic Warfare: Issues and Challenges for Emitter Classification”, Def. Sci. J. 201161(3), 228‒234 (2011).
  3.  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).
  4.  Q. Xu, R. Zheng, W. Saad, and Z. Han, “Device Fingerprinting in Wireless Networks: Challenges and Opportunities”, IEEE Commun. Surv. Tutor. 18(1), 94‒104 (2016).
  5.  P.C. Adam and G.L. Dennis, “Identification of Wireless Devices of Users Who Actively Fake Their RF Fingerprints With Artificial Data Distortion”, IEEE Trans. Wirel. Commun. 14(11), 5889‒5899 (2015).
  6.  N. Zhou, L. Luo, G. Sheng, and X. Jiang, “High Accuracy Insulation Fault Diagnosis Method of Power Equipment Based on Power Maximum Likelihood Estimation”, IEEE Trans. Power Deliv. 34(4), 1291‒1299 (2019).
  7.  S. Guo, R.E. White, and M. Low, “A comparison study of radar emitter identification based on signal transients”, IEEE Radar Conference, Oklahoma City, 2018, pp. 286‒291.
  8.  Q. Wu, C. Feres, D. Kuzmenko, D. Zhi, Z. Yu, and X. Liu, “Deep learning based RF fingerprinting for device identification and wireless security”, Electron. Lett. 54(24), 1405‒1407 (2018).
  9.  A. Kawalec, R. Owczarek, and J. Dudczyk, “Karhunen-Loeve transformation in radar signal features processing”, International Conference on Microwaves, Krakow, 2006.
  10.  B. Danev and S. Capkun, “Transient-based identification of wireless sensor nodes”, Information Processing in Sensor Networks, San Francisco, 2009, pp. 25‒36.
  11.  R.W. Klein, M.A. Temple, M.J. Mendenhall, and D.R. Reising, “Sensitivity Analysis of Burst Detection and RF Fingerprinting Classification Performance”, International Conference on Communications, Dresden, 2009, pp. 641‒645.
  12.  C. Bertoncini, K. Rudd, B. Nousain, and M. Hinders, “Wavelet Fingerprinting of Radio-Frequency Identification (RFID) Tags”, I IEEE Trans. Ind. Electron. 59(12), 4843‒4850 (2012).
  13.  Z. Shi, X. Lin, C. Zhao, and M. Shi, “Multifractal slope feature based wireless devices identification”, International Conference on Computer Science and Education, Cambridge, 2015, pp. 590‒595.
  14.  C.K. Dubendorfer, B.W. Ramsey, and M.A. Temple, “ZigBee Device Verification for Securing Industrial Control and Building Automation Systems”, International Conference on Critical Infrastructure Protection ,Washington DC, 2013, pp. 47‒62.
  15.  D.R. Reising and M.A. Temple, “WiMAX mobile subscriber verification using Gabor-based RF-DNA fingerprints”, International Conference on Communications, Ottawa, 2012, pp. 1005‒1010.
  16.  Y. Li, Y. Zhao, L. Wu, and J. Zhang, “Specific emitter identification using geometric features of frequency drift curve”, Bull. Pol. Acad. Sci. Tech. Sci. 66, 99‒108 (2018).
  17.  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).
  18.  T.L. Carroll, “A nonlinear dynamics method for signal identification”, Chaos Interdiscip. J. Nonlinear Sci. 17(2), 023109 (2007).
  19.  D. Sun, Y. Li, Y. Xu, and J. Hu, “A Novel Method for Specific Emitter Identification Based on Singular Spectrum Analysis”, Wireless Communications & Networking Conference, San Francisco, 2017, pp. 1‒6.
  20.  Y. Jia, S. Zhu, and G. Lu, “Specific Emitter Identification Based on the Natural Measure”, Entropy 19(3), 117 (2017).
  21.  L. Lacasa, B. Luque, J. Luque, and J.C. Nuno, “The visibility graph: A new method for estimating the Hurst exponent of fractional Brownian motion”, Europhys. Lett. 86(3), 30001‒30005 (2009).
  22.  M. Ahmadlou and H. Adeli, “Visibility graph similarity: A new measure of generalized synchronization in coupled dynamic systems”, Physica D 241(4), 326‒332 (2012).
  23.  S. Zhu and L. Gan, “Specific emitter identification based on horizontal visibility graph”, IEEE International Conference Computer and Communications, Chengdu, 2017, pp. 1328‒1332.
  24.  B. Luque, L. Lacasa, F. Ballesteros, and J. Luque, “Horizontal visibility graphs: Exact results for random time series”, Phys. Rev. E. 80(4), 046103 (2009).
  25.  W. Jiang, B. Wei, J. Zhan, C. Xie, and D. Zhou, “A visibility graph power averaging aggregation operator: A methodology based on network analysis”, Comput. Ind. Eng. 101, 260‒268 (2016).
  26.  M. Wajs, P. Kurzynski, and D. Kaszlikowski, “Information-theoretic Bell inequalities based on Tsallis entropy”, Phys. Rev. A. 91(1), 012114 (2015).
  27.  J. Liang, Z. Huang, and Z. Li, “Method of Empirical Mode Decomposition in Specific Emitter Identification”, Wirel. Pers. Commun. 96(2), 2447‒2461, (2017).
  28.  A.M. Ali, E. Uzundurukan, and A. Kara, “Improvements on transient signal detection for RF fingerprinting”, Signal Processing and Communications Applications Conference (SIU), Antalya, 2017, pp. 1‒4.
  29.  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).
  30.  D.R. Kong and H.B. Xie, “Assessment of Time Series Complexity Using Improved Approximate Entropy”, Chin. Phys. Lett. 28(9), 90502‒90505 (2011).
  31.  T. Chen and C. Guestrin, “XGBoost: A Scalable Tree Boosting System”, Knowledge Discovery and Data Mining, San Francisco, 2016, pp. 785‒794.
  32.  G. Huang, Y. Yuan, X. Wang, and Z. Huang, “Specific Emitter Identification Based on Nonlinear Dynamical Characteristics”, Can. J. Electr. Comp. Eng.-Rev. Can. Genie Electr. Inform. 39(1), 34‒41 (2016).
  33.  D. Sun, Y. Li, Y. Xu, and J. Hu, “A Novel Method for Specific Emitter Identification Based on Singular Spectrum Analysis”, Wireless Communications and Networking Conference, San Francisco, 2017, pp. 1‒6.

Date

16.03.2021

Type

Article

Identifier

DOI: 10.24425/bpasts.2021.136741

Source

Bulletin of the Polish Academy of Sciences: Technical Sciences; 2021; 69; 2; e136741
×