Specific emitter identification based on graphical representation of the distribution of radar signal parameters

• Autorzy: Dudczyk J. , Kawalec A.

Identyfikatory

DOI

10.1515/bpasts-2015-0044

• Języki publikacji: EN

Abstrakty

EN

The article presents some possibilities of same type radar copies identification with the use of graphical representation. The procedure described by the authors is based on transformation and analysis of basic parameters distribution which are measured by the radar signal especially Pulse Repetition Interval. A radar intercept receiver passively collects incoming pulse samples from a number of unknown emitters. Information such as Pulse Repetition Interval, Angle of Arrival, Pulse Width, Radio Frequency and Doppler shifts are not usable. The most important objectives are to determine the number of emitters present and classify incoming pulses according to emitters. To classify radar emitters and precisely identification the copy of the same type of an emitter source in surrounding environment, we need to explore the detailed structure i.e. intra-pulse information, unintentional radiated electromagnetic emission and fractal features of a radar signal. An emitter has its own signal structure. This part of radar signal analysis is called Specific Emitter Identification. Utilization of some specific properties of electronic devices can cause heightening probability of a correct identification.

Słowa kluczowe

EN

Specific Emitter Identification; SEI; radar recognition; ELINT system; Electronics Warfare System; EWS

PL

SEI; radar; system ELINT; system EWS

• Wydawca: Polska Akademia Nauk, Wydział IV Nauk Technicznych
• Czasopismo: Bulletin of the Polish Academy of Sciences. Technical Sciences
• Rocznik: 2015
• Tom: Vol. 63, nr 2
• Strony: 391--396
• Opis fizyczny: Bibliogr. 18 poz., wykr.

Twórcy

autor

Dudczyk J.

• WB Electronics S.A., 129/133 Poznańska St., 05-850 Ożarów Mazowiecki, Poland / Institute of Radioelectronics, Faculty of Electronics, Military University of Technology, 2 S. Kaliskiego 2 St., 00-908 Warsaw, Poland

autor

Kawalec A.

• WB Electronics S.A., 129/133 Poznańska St., 05-850 Ożarów Mazowiecki, Poland / Institute of Radioelectronics, Faculty of Electronics, Military University of Technology, 2 S. Kaliskiego 2 St., 00-908 Warsaw, Poland

Bibliografia

• [1] R.G. Willey, Electronic Intelligence: The Analysis of Radar Signals, second edition. Artech House, London, 1993.
• [2] J. Dudczyk, “Applying the radiated emission to the radioelectronic devices identification”, Dissertation Thesis, Dept. Elect., Military Univ. of Tech., Warsaw, 2004, (in Polish).
• [3] R. Barker, Relationship Modeling, Addison -Wesley Publishers, Wokingham, 1989.
• [4] J. Dudczyk and A. Kawalec, “Fractal feature of specific emitter identification”, Acta Phys. Pol. A 124 (3), 406-409 (2013).
• [5] 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).
• [6] M.W. Liu and J.F. Doherty, “Specific emitter identification using nonlinear device estimation”, Proc. IEEE Sarnoff Symp. 1, 1-5 (2008).
• [7] K.I. Talbot, P.R. Duley, and M.H. Hyatt, “Specific emitter identification and verification”, Technology Review J. 1, 113-133 (2003).
• [8] K. Murawski, K. Rożanowski, and M. Krej, “Research and parameter optimization of the pattern recognition algorithm for the eye tracking infrared sensor”, Acta Phys. Pol. A 124 (3), 513-516 (2013).
• [9] K. Murawski and K. Rożanowski, “Pattern recognition algorithm for eye tracker sensor video data analysis”, Acta Phys. Pol. A 124 (3), 509-512 (2013).
• [10] S. De Backer, A. Naud, and P. Scheuders, “ Non-linear dimensionality reduction techniques for unsupervised feature extraction”, Pattern Recognition Letters 19, 711-720 (1998).
• [11] Z. Piotrowski and K. Rożanowski, “Robust algorithm for Heart Rate (HR) detection and Heart Rate Variability (HRV) estimation”, Acta Phys. Pol. A 118 (1), 131-135 (2010).
• [12] T.C. Havens, J.C. Bezdek, C. Leckie, L.O. Hall, and M. Palaniswami, “Fuzzy c-means algorithms for very large data”, IEEE Trans. Fuzzy Systems 44 (6), 2361-2373 (2006).
• [13] J. Dudczyk, A. Kawalec, and J. Cyrek, “Applying the distance and similarity functions to the radar signals identification”, Proc. IEEE Int. Radar Symp. 1, 1-4 (2008).
• [14] R.O. Duda, P.E. Hart, and D.G. Stork, Pattern Classification. John Wiley & Sons, New York, 2000.
• [15] K. Fukunaga, Introduction to Statistical Pattern Recogniction, second edition, Academic Press, New York, 1990.
• [16] S. Theodoridis and K. Koutroumbas, Pattern Recognition, Academic Press, San Diego, 1999.
• [17] F. Berizzi, G. Bertini, and M. Martorella, “Two-dimensional variation algorithm for fractal analysis of sea SAR images”, IEEE Trans. Geosci. Remote Sens. 44, 2361-2373 (2006).
• [18] M. German, G.B. Be’nie’, and J.M. Boucher, “Contribution of the fractal dimension to multiscale adaptive filtering of SAR imagery”, IEEE Trans. Geosci. Remote Sens. 41, 1765-1772 (2003).