Fast-decision identification algorithm of emission source pattern in database

• Autorzy: Dudczyk J. , Kawalec A.

Identyfikatory

DOI

10.1515/bpasts-2015-0043

• Języki publikacji: EN

Abstrakty

EN

This article presents Fast-decision Identification Algorithm (FdIA) of Source Emission (SE) in DataBase (DB). The aim of this identification process is to define signal vector (V) in the form of distinctive features of this signal which is received in the process of its measurement. Superheterodyne ELectronic INTelligence (ELINT) receiver in the measure procedure was used. The next step in identification process is comparison vector with pattern in DB and calculation of decision function. The aim of decision function is to evaluate similarity degree between vector and pattern. Identification process mentioned above differentiates copies of radar of the same type which is a special test challenge defined as Specific Emitter Identification (SEI). The authors of this method drew up FdIA and three-stage parameterization by the implementation of three different ways of defining the degree of similarity between vector and pattern (called ’Compare procedure’). The algorithm was tested on hundreds of signal vectors coming from over a dozen copies of radars of the same type. Fast-decision Identification Algorithm which was drawn up and implemented makes it possible to create Knowledge Base which is an integral part of Expert DataBase. As a result, the amount of the ambiguity of decisions in the process of Source Emission Identification is minimized.

Słowa kluczowe

EN

Fast-decision Identification Algorithm; Specific Emitter Identification; Emitter Pattern; database; superheterodyne ELINT receiver

PL

baza danych; superheterodynowy odbiornik ELINT; algorytm FdIA

• 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: 385--389
• Opis fizyczny: Bibliogr. 19 poz., rys., tab., 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] T.J. Teorey, S.S. Lightstone, T. Nadeau, and H.V Jagadish, Database Modeling and Design, Morgan Kaufmann Publishers, Elsevier, Burlington, 2011.
• [2] C. Zhang, H. Yang, W. Hu, and W. Yu, “Ship formation recognition based on information fusion of spaceborne IMINT and ELINT”, Proc. SPIE 6795, CD-ROM (2007).
• [3] E. Świercz, “Automatic classification of LFM signals for radar emitter recognition using wavelet decomposition and LVQ classifier”, Acta Physica Polonica A 119 (4), 488-494. (2011).
• [4] R.O. Duda, P.E. Hart, and D.G. Stork, Pattern Classification, Wiley & Sons Inc. NY, 2000.
• [5] K. Fukunaga, Introduction to Statistical Pattern Recogniction, Academic Press, San Diego, 1990.
• [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] 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).
• [9] S. D’Agostino, G. Foglia, and D. Pistoia, “Specific emitter identification: analysis on real radar signal data”, Proc. Eu- RAD 1, 242-245 (2009).
• [10] F.C. Gu, H.C. Chang, F.H. Chen, C.C. Kuo, and C.H. Hsu, “Application of the Hilbert-Huang transform with fractal feature enhancement on partial discharge recognition of power cable joints”, IET Science, Measurement and Technology 6 (6), 440-448 (2012).
• [11] B. Świdzińska, “Fractal compression using random encoding algorithm”, Bull. Pol. Ac.: Tech. 46 (4), 525-532 (1998).
• [12] M. Conning and F. Potgieter, “Analysis of measured radar data for Specific Emitter Identification”, Proc. IEEE Radar Conf. 1, 35-38 (2010).
• [13] J. Dudczyk, A. Kawalec, and R. Owczarek, “An application of iterated function system attractor for specific radar source identification”, Proc. IEEE Microwaves, Radar and Wireless Communications 1, 1-4 (2008).
• [14] K.D. Kang, S.H. Son, and J.A. Stanovic, “Managing deadline miss ratio and sensor data freshness in real-time databases”, IEEE Trans. Knowledge and Data Engineering 16 (10), 1200-1216 (2004).
• [15] R. Barker, CASE Method: Entity Relationship Modelling, Addison-Wesley Longman Publishing Co., Boston, 1990.
• [16] T.J. Teorey, D. Yang, and J.P. Fry, “A logical design methodology for relational databases using the extended entityrelationship model”, J. ACM Computing Surveys 18 (2), 197-222 (1986).
• [17] P. Chen, “The entity-relationship model-toward a unified view of data”, J. ACM Trans. on Database Systems 1 (1), 9-36 (1976).
• [18] 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).
• [19] J. Dudczyk, A. Kawalec, and J. Cyrek, “Applying the distance and similarity functions to radar signals identification”, Proc. IEEE Int. Radar Symp. 1, 1-4 (2008).