PL EN


Preferencje help
Widoczny [Schowaj] Abstrakt
Liczba wyników
Tytuł artykułu

Specific emitter identification using geometric features of frequency drift curve

Autorzy
Treść / Zawartość
Identyfikatory
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Specific emitter identification (SEI) is a technique for recognizing different emitters of the same type which have the same modulation parameters. Using only the classic modulation parameters for recognition, one cannot distinguish different emitters of a same type. To solve the problem, new features urgently need to be developed for recognition. This paper focuses on the common phenomenon of frequency drift, defines geometric features of frequency drift curve and, finally, proposes a practical algorithm of specific emitter identification using the geometric features. The proposed algorithm consists of three processes: instantaneous frequency estimation based on the adaptive fractional spectrogram, feature extraction of frequency drift curve based on geometric methods for describing a curve and recognition process based on support vector machine. Simulation results show that the identification rate is generally more than 98% above –5 dB of signal to noise ratio (SNR), and real data experiment verifies the practical performance of the proposed algorithm.
Rocznik
Strony
99--108
Opis fizyczny
Bibliogr. 30 poz., rys., wykr., tab.
Twórcy
autor
  • School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China
autor
  • School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China
autor
  • School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin, China
autor
  • R&D Centre, Chinese Academy of Launch Vehicle Technology, Beijing, China
Bibliografia
  • [1] L.E. Langley, “Specific emitter identification (SEI) and classical parameter fusion technology”, in WESCON/’93. Conference Record, 1993, 377‒381.
  • [2] K.I. Talbot, P.R. Duley, and M.H. Hyatt, “Specific emitter identification and verification”, Technology Review Journal, pp. 113‒133, 2003.
  • [3] M.-W. Liu and J.F. Doherty, “Nonlinearity estimation for specific emitter identification in multipath channels”, IEEE Transactions on Information Forensics and Security 6, 1076‒1085 (2011).
  • [4] A. Kawalec and R. Owczarek, “Specific emitter identification using intrapulse data”, in Radar Conference, 2004. EURAD. First European, 249‒252 (2004).
  • [5] J. Matuszewski, “Specific emitter identification”, International Radar Symposium, 1‒4 (2008).
  • [6] A.G. Stove, A.L. Hume, and C.J. Baker, “Low probability of intercept radar strategies”, IEE Proceedings – Radar, Sonar and Navigation 151, 249 (2004).
  • [7] V. Krishnamurthy, “Emission management for low probability intercept sensors in network centric warfare”, IEEE Transactions on Aerospace and Electronic Systems 41, 133‒152 (2005).
  • [8] K.-W. Lee and W.-K. Lee, “The low probability of intercept RADAR waveform based on random phase and code rate transition for Doppler tolerance improvement”, The Journal of Korean Institute of Electromagnetic Engineering and Science 26, 999‒1011 (2015).
  • [9] J. Dudczyk and A. Kawalec, “Specific emitter identification based on graphical representation of the distribution of radar signal parameters”, Bull. Pol. Ac.: Tech 63(2), 391‒396, (2015).
  • [10] H. Ye, Z. Liu, and W. Jiang, “Comparison of unintentional frequency and phase modulation features for specific emitter identification”, Electronics Letters 48, 875‒876, (2012).
  • [11] J. Zhang, F. Wang, O.A. Dobre, and Z. Zhong, “Specific emitter identification via Hilbert-Huang transform in single-hop and relaying scenarios”, IEEE Transactions on Information Forensics and Security 11, 1192‒1205 (2016).
  • [12] Y. Yuan, Z. Huang, H. Wu, and X. Wang, “Specific emitter identification based on Hilbert-Huang transform-based time-frequency-energy distribution features”, IET Communications 8, 2404‒2412 (2014).
  • [13] A. Kawalec, R. Owczarek, and J. Dudczyk, “Data modeling and simulation applied to radar signal recognition”, Molecular and Quantum Acoustics 26, 165‒173 (2005).
  • [14] R. Samborski and M. Ziolko, “Speaker localization in conferencing systems employing phase features and wavelet transform”, 2013 IEEE International Symposium on Signal Processing and Information Technology (IEEE Isspit 2013), 333‒337 (2013).
  • [15] H. Jiang, W. Guan, and L. Ai, “Specific radar emitter identification based on a digital channelized receiver”, 5th International Congress on Image and Signal Processing (Cisp), 1855‒1860, (2012).
  • [16] Z. Tang and S. Li, “Steady signal-based fractal method of specific communications emitter sources identification”, in Wireless Communications, Networking and Applications, Wcna 2014. vol. 348, eds. Q.A. Zeng, 809‒819 (2016).
  • [17] G. Huang, Y. Yuan, X. Wang, and Z. Huang, “Specific emitter identification based on nonlinear dynamical characteristics”, Canadian Journal of Electrical and Computer Engineering-Revue Canadienne De Genie Electrique Et Informatique 39, 34‒41, (2016).
  • [18] J. Dudczyk and A. Kawalec, “Identification of emitter sources in the aspect of their fractal features”, Bull. Pol. Ac.: Tech 61(3), 623‒628, (2013).
  • [19] J. Dudczyk and A. Kawalec, “Fast-decision identification algorithm of emission source pattern in database”, Bull. Pol. Ac.: Tech 63(2), 385‒389 (2015).
  • [20] Y. Shi and H. Ji, “Kernel canonical correlation analysis for specific radar emitter identification”, Electronics Letters 50, 1318‒1319 (2014).
  • [21] L. Li, H.B. Ji, and L. Jiang, “Quadratic time-frequency analysis and sequential recognition for specific emitter identification”, IET Signal Processing 5, 568‒574 (2011).
  • [22] A. Aubry, A. Bazzoni, V. Carotenuto, A. De Maio, and P. Failla, “Cumulants-based radar specific emitter identification”, in Information Forensics and Security (WIFS), 2011 IEEE International Workshop on, 1‒6 (2011).
  • [23] A. Kawalec and R. Owczarek, “Radar emitter recognition using intrapulse data”, in Microwaves, Radar and Wireless Communications, 15th International Conference on, vol. 2, 435‒438 (2004).
  • [24] I. Tahir, A. Dexter, and R. Carter, “Noise performance of frequency-and phase-locked CW magnetrons operated as current-controlled oscillators”, IEEE Transactions on Electron Devices 52, 2096‒2103 (2005).
  • [25] N.A. Khan and B. Boashash, “Instantaneous frequency estimation of multicomponent nonstationary signals using multiview time-frequency distributions based on the adaptive fractional spectrogram”, IEEE Signal Processing Letters 20, 157‒160 (2013).
  • [26] B. Barkat and K. Abed-Meraim, “Algorithms for blind components separation and extraction from the time-frequency distribution of their mixture”, Eurasip Journal on Applied Signal Processing, 2025‒2033 (2004).
  • [27] C. Capus and K. Brown, “Short-time fractional Fourier methods for the time-frequency representation of chirp signals”, The Journal of the Acoustical Society of America 113, 3253‒3263, (2003).
  • [28] G.Q. Lu, H.G. Xu, and Y.B. Li, “Line detection based on chain code detection”, 2005 IEEE International Conference on Vehicular Electronics and Safety Proceedings, 98‒103, (2005).
  • [29] H.H. Chi, H.W. Sun, and X.J. Yang, “Curve tracking with improved chain code algorithm”, 2009 IEEE International Conference on Mechatronics and Automation, Vols 1‒7, Conference Proceedings, 4333‒4338 (2009).
  • [30] C.J.C. Burges, “A tutorial on support vector machines for pattern recognition”, Data Mining and Knowledge Discovery 2, 121‒167 (1998).
Uwagi
PL
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2018).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-a27a14fb-410f-4ab5-bbb8-dc3894533ee8
JavaScript jest wyłączony w Twojej przeglądarce internetowej. Włącz go, a następnie odśwież stronę, aby móc w pełni z niej korzystać.