Details
Title
Accurate identification on individual similar communication emitters by using HVG-NTE featureJournal title
Bulletin of the Polish Academy of Sciences Technical SciencesYearbook
2021Volume
69Issue
2Authors
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, ChinaKeywords
communication emitter ; identification ; feature extraction ; HVG ; NTEDivisions of PAS
Nauki TechniczneCoverage
e136741Bibliography
- J. Dudczyk, “Radar emission sources identification based on hierarchical agglomerative clustering for large data sets”, J. Sens. 2016, 1879327 (2016).
- G. Manish, G. Hareesh, and M. Arvind, “Electronic Warfare: Issues and Challenges for Emitter Classification”, Def. Sci. J. 201161(3), 228‒234 (2011).
- 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).
- 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).
- 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).
- 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).
- 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.
- 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).
- A. Kawalec, R. Owczarek, and J. Dudczyk, “Karhunen-Loeve transformation in radar signal features processing”, International Conference on Microwaves, Krakow, 2006.
- B. Danev and S. Capkun, “Transient-based identification of wireless sensor nodes”, Information Processing in Sensor Networks, San Francisco, 2009, pp. 25‒36.
- 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.
- 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).
- 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.
- 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.
- 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.
- 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).
- 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).
- T.L. Carroll, “A nonlinear dynamics method for signal identification”, Chaos Interdiscip. J. Nonlinear Sci. 17(2), 023109 (2007).
- 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.
- Y. Jia, S. Zhu, and G. Lu, “Specific Emitter Identification Based on the Natural Measure”, Entropy 19(3), 117 (2017).
- 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).
- 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).
- S. Zhu and L. Gan, “Specific emitter identification based on horizontal visibility graph”, IEEE International Conference Computer and Communications, Chengdu, 2017, pp. 1328‒1332.
- 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).
- 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).
- M. Wajs, P. Kurzynski, and D. Kaszlikowski, “Information-theoretic Bell inequalities based on Tsallis entropy”, Phys. Rev. A. 91(1), 012114 (2015).
- J. Liang, Z. Huang, and Z. Li, “Method of Empirical Mode Decomposition in Specific Emitter Identification”, Wirel. Pers. Commun. 96(2), 2447‒2461, (2017).
- 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.
- 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).
- D.R. Kong and H.B. Xie, “Assessment of Time Series Complexity Using Improved Approximate Entropy”, Chin. Phys. Lett. 28(9), 90502‒90505 (2011).
- T. Chen and C. Guestrin, “XGBoost: A Scalable Tree Boosting System”, Knowledge Discovery and Data Mining, San Francisco, 2016, pp. 785‒794.
- 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).
- 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.