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ANALYSIS OF METHODS FOR RECOGNITION OF TYPES OF PULSE REPETITION INTERVAL MODULATION OF RADAR SIGNALS
Denis S. Chirov, Moscow Technical University of Communications and Informatics, Moscow, Russia, chirov@srd.mtuci.ru
Ekaterina O. Kandaurova, Moscow Technical University of Communications and Informatics, Moscow, Russia
Abstract
The modern development of radio and radar facilities leads to the need for special radio monitoring tools to control the electromagnetic spectrum. In the process of radio monitoring there is a need to detect and identify (recognition) sources of radio radiation, in particular radars. The peculiarity of the pulse radar station is that the emitted radar signals can have both intra-pulse and pulse repetition interval (PRI) modulation. The article considers the existing methods of recognition of the main types of PRI modulation of radar signals. Currently, the most common types of PRI modulation of radar signals are: constant, stagger, sliding, dwell and switch, jittered and periodic. Different methods (based on artificial neural networks, logical rules base, wavelet analysis, histogram analysis) are used to recognize these types of modulation, using different sets of recognition features. Each of these methods has its advantages and disadvantages. The histogram method provides recognition of the largest number of types of PRI modulation [18, 19]. However, this method has not been investigated for resistance to noise and interference. The method based on a long short-term memory provides the best stability in noise, but provides recognition of fewer types of PRI modulation. It seems expedient in the framework of further research to assess the effectiveness of the ensemble of all the considered features of recognition of types of PRI modulation of radar signals in different conditions of SNR environment for the synthesis of the dictionary of the most informative features.
Keywords: pulse repetition interval modulation, radar, recognition, artificial neural networks, wavelet analysis, histogram method.
References
1. Gorelik A.L., Skrippkin V.A. (1984). Methods of recognition. 2-nd ed. Moscow: Vishaya shkola. 208 p. (In Russian)
2. Tou Ju., Gonzales R. (1978). Pattern recognition principles. Moscow: Mir. 414 p. (In Russian)
3. Adjemov S.S., Klenov N.V., Tereshonok M.V., Chirov D.S. The use of artificial neural networks for classification of signal sources in cognitive radio systems. Programming and Computer Software. 2016. Vol. 42. No. 3. pp. 121-128.
4. Adjemov S.S., Tereshonok M.V., Chirov D.S. Neural network method for recognition of radio signal modulation types using high-order cumulants. T-Comm, no. 9-2012, pp. 9-12. (In Russian)
5. Zadeh A.E. (2010). Automatic recognition of radio signals using a hybrid intelligent technique. Expert Systems with Applications 37, pp. 5803-5812.
6. Hong L., Ho K.C.(1999). Identification of digital modulation types using the wavelet transform. IEEE MILCOM, pp. 427-431.
7. Chirov D.S., Vynogradov A.N., Vorobyova E.O. (2018). Application of the decision trees to recognize the types of digital modulation of radio signals in cognitive systems of HF communication. Systems of Signal Synchronization, Generating and Processing in Telecommunications (SYNCHROINFO), 4-5 July 2018, pp. 1-6.
8. Swami A., Sadler B. (2000). Hierarchical digital modulation classification using cumulants. IEEE Trans. Commun., vol. 48, no. 3, pp. 416-428, March 2000.
9. McShea R.E. (2014). «Electronic Warfare». eng. In: Test and Evaluation of Aircraft Avionics and Weapon Systems. 2nd Edition. Institution of Engineering and Technology. ISBN: 978-1-61353-176-1.
10. Noone G.P. (1999). «A neural approach to automatic pulse repetition interval modulation recognition». 1999 Information, Decision and Control. Data and Information Fusion Symposium, Signal Processing and Communications Symposium and Decision and Control Symposium. Proceedings (Cat. No. 99EX251), pp. 213-218.
11. Kenan Gencol, A.T. Nuray, Ali Kara. (2016). A wavelet-based feature set for recognizing pulse repetition interval modulation patterns. Turk J Elec Eng & Comp Sci ,24, pp. 3078-3090.
12. Mardia H.K. (1989). «New techniques for the deinterleaving of repetitive sequences». IEE Proceedings F — Radar and Signal Processing 136.4, pp. 149-154. ISSN: 0956-375X. DOI: 10.1049/ip-f-2.1989.0025.
13. F. Katsilieris, S. Apfeld, and A. Charlish. (2017). Correlation based classification of complex PRI modulation types. Sensor Signal Processing for Defence Conference, SSPD 2017: London, UK, 6-7 December 2017.
14. Liu Y. and Q. Zhang (2017). «An improved algorithm for PRI modulation recognition». 2017 IEEE International Conference on Signal Processing, Communications and Computing (ICSPCC), pp. 1-5.
15. Norgen E. Pulse Repetition Interval Modulation Classification using Machine Learning. Master in Computer Science. August 19, 2018. School of Electrical Engineering and Computer Science. 56 p.
16. Sepp Hochreiter; Jergen Schmidhuber (1997). «Long short-term memory». Neural Computation. 9 (8), pp. 1735-1780.
17. Haykin S. (2006). Neural Networks. A comprehensive Foundation. 2nd ed. Moscow: Williams Publishing Hous. 1104 p. (In Russian)
18. Kauppi, J.-P.,&Martikainen, K. (2007). An efficient set of features for pulse repetition interval modulation recognition. Proceedings of IET international conference on radar systems. 19. J.-P. Kauppi et al. Neural Networks 23 (2010), pp. 1226-1237.
20. Botev Z.I., Grotowski J.F., Kroese D.P. (2010). Kernel density estimation via diffusion. Annals of Statistics. 38 (5), pp. 2916-2957.
Information about authors:
Denis S. Chirov, professor of the department of radio systems, doctor of technical sciences, Moscow Technical University of communications and Informatics, Moscow, Russia
Ekaterina O. Kandaurova, engineer, Moscow Technical University of communications and Informatics, Moscow, Russia