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Telecommunications and Radio Engineering
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Telecommunications and Radio Engineering

DOI: 10.1615/TelecomRadEng.v78.i19.80
pages 1759-1770

ULTRASHORT IMPULSE RADAR FOR DETECTION AND CLASSIFICATION OF OBJECTS IN LAYERED MEDIUM BY ARTIFICIAL NEURAL NETWORK

Oleksandr Dumin
V. Karazin National University of Kharkiv, 4 Svobody Sq., Kharkiv 61022, Ukraine
V. Plakhtii
V. Karazin National University of Kharkiv, 4 Svobody Sq., Kharkiv 61022, Ukraine
O. Prishchenko
V. Karazin National University of Kharkiv, 4 Svobody Sq., Kharkiv 61022, Ukraine
D. Shyrokorad
Zaporizhzhya National Technical University, 64 Zhukovskogo St., 69063, Zaporizhzhya, Ukraine
V. A. Katrich
V. Karazin National University of Kharkiv, 4 Svobody Sq., Kharkiv 61022, Ukraine

RÉSUMÉ

The application of artificial neural networks (ANN) for object classification based on data received by impulse radar is analyzed. Instead of spectrum analysis or threshold criterion application, the amplitudes of received impulse electromagnetic field in a definite time and spatial points are processed by the neural network directly to make an object recognition procedure. Several electromagnetic problems and structures of ANN are considered as examples to obtain the layered medium parameter, to find some object and its coordinates in a medium. The stability of the determination of object placement and medium geometrical characteristics are studied in the presence of experimental errors and noises. The normal incidence of short impulse plane wave of Gaussian time form on the complex medium with losses is considered. Finite Difference in Time Domain (FDTD) method is used for obtaining the reflected electromagnetic field above a medium. The amplitudes of the electrical component of the field are the input data for ANN. The thicknesses of one of the layers or distances to an object are the ANN output parameters that are sought for.

RÉFÉRENCES

  1. Immoreev, I., Samkov, S., and Teh-HoTao, (2005) Short-Distance Ultra-Wideband Radars, IEEE Aerospace and Electronic Systems Magazine, 20(6), pp. 9-14.

  2. Harmuth, H., (1981) Nonsinusoidal Waves for Radar and Radiocommunications, Academic Press, New York.

  3. Callan, R., (1999) The Essence of Neural Networks, Prentice Hall Europe, New York.

  4. Shyrokorad, D., Dumin, O., and Dumina, O., (2008) Time domain analysis of reflected impulse fields by artificial neural network, Proc. IV Conf. on UWBUSIS, Sevastopol, Ukraine, pp. 124-126.

  5. Haykin, S., (1999) Neural Networks, Prentice-Hall, New Jersey.

  6. Daniels, D.J., (2004) Ground Penetrating Radar, IEEE, London.

  7. Hai-Zhong, Yu., Yu-Feng, Ouyang., and Hong, Chen., (2012) Application of Ground Penetrating Radar to Inspect the Metro Tunnel, 14-th International Conference on Ground Penetrating Radar (GPR), Shanghai, China, pp 759-763.

  8. Morgenthaler, A. and Rappaport, C., (2013) Fast GPR Underground Shape Anomaly Detection Using The Semi-Analytic Mode Matching (Samm) Algorithm, IGARSS, pp. 1422-1425.

  9. Turk, A.S., Hocaoglu, K.A., and Vertiy, A.A., (2011) Subsurface Sensing, Hoboken: Wiley.

  10. Cook, J.C., (1960) Proposed monocycle-pulse very high frequency radar for airborne ice and snow measurement, Trans. AIEE Commun. Electron., 79, pp. 588-594.

  11. Varyanitsa-Roshchupkina, L.A. and Pochanin, G.P., (2007) Video Pulse Electromagnetic Wave Diffraction on Subsurface Objects, Telecommunications and Radio Engineering, 66(5), pp. 391-414.

  12. Pochanin, G., Masalov, S., Pochanina, I., Capineri, L. et al., (2016) Modern Trends in Development and Application of the UWB Radar Systems, Proc. 8th International Conference on Ultrawideband and Ultrashort Impulse Signals, , Odesa, Ukraine, pp. 7-115-11.

  13. Prabhat Sharma, Bambam Kumar, Dharmendra Singh, and Gaba, S.P., (2016) Non-metallic pipe detection using SF-GPR: a new approach using Neural Network, IGARSS, pp. 6609-6612.

  14. Pochanin, G.P., Ruban, V.P., Kholod, P.V., Shuba, O.A. et al., (2014) Advances in ground penetrating radars for road surveying, Ultrawideband and Ultrashort Impulse Signals, Kharkiv, Ukraine, pp. 13-18.

  15. Earp, S.L., Hughes, E.S., Elkins, T.J., and Vickers, R., (1996) Ultra-Wideband Ground-Penetrating Radar For the Detection of Buried Metallic Mines, IEEE National Radar Conference, pp. 7-12, Ann Arbor, Michigan, 13-16 May.

  16. Dumin, O., Dumina, O., and Shyrokorad, D., (2009) Time domain analysis of fields reflected from model of human body surface using artificial neural network, Proc. EuCAP, pp. 235-238, Berlin.

  17. Shyrokorad, D., Dumin, O., Dumina O., and Katrich V., (2010) Analysis of Transient Fields Reflected From Model of Human Body Surface Using Convolutional Neural Network, Proc. MMET, Kyiv, Ukraine.

  18. Shyrokorad, D., Dumin, O., Dumina O., Katrich, V., and Chebotarev, V., (2010) Approximating properties of artificial neural network in time domain for the analysis of electromagnetic fields reflected from model of human body surface, Proc. MSMW, Kharkiv, Ukraine.

  19. Andreev, M.V. and Drobakhin, O.O., (2016) Feature of Prony's Method Application for Natural Frequencies Estimation from the Frequency Response, Proc. of 8th International Conference on Ultrawideband and Ultrashort Impulse Signals, Odesa, Ukraine, pp. 18-20.

  20. Dumin, O., Prishchenko, O., Pochanin, G., Plakhtii, V., and Shyrokorad, D., (2018) Subsurface Object Identification by Artificial Neural Networks and Impulse Radiolocation, IEEE Second International Conference on Data Stream Mining & Processing (DSMP-2018), Lviv, Ukraine, pp. 434-437, August 21-25.

  21. Dumin, O.M., Prishchenko, O., Shyrokorad, D., and Plakhtii, V., (2018) Application of UWB Electromagnetic Waves for Subsurface Object Location Classification by Artificial Neural Networks, Proc. 9-th International Conference on Ultrawideband and Ultrashort Impulse Signals (UWBUSIS), Odesa, Ukraine, pp. 290-293.

  22. Dumin, O.M., Plakhtii, V.A., Shyrokorad, D.V., and Prishchenko, O.A., (2019) Signal Processing in UWB Subsurface Radiolocation by Artificial Neural Networks, Proc. IEEE International Scientific and Practical Conference Problems of Infocommunications Science and Technology (PIC S&T2019), Kyiv, Ukraine, pp. 384-387.

  23. Plakhtii, V., Dumin, O., Prishchenko, O., Shyrokorad, D., and Pochanin, G., (2019) Influence of Noise Reduction on Object Location Classification by Artificial Neural Networks for UWB Subsurface Radiolocation, Proc. DIPED, Lviv, Ukraine, pp. 64-68.

  24. Dumin, O., Plakhtii, V., Shyrokorad, D., Prishchenko, O., and Pochanin, G., (2019) UWB subsurface radiolocation for object location classification by artificial neural networks based on discrete tomography approach, Proc. IEEE 2nd Ukraine Conference on Electrical and Computer Engineering (UKRCON-2019), Lviv, Ukraine, pp. 182-187.

  25. Taflove, A. and Hagness, S., (2005) Computational Electrodynamics: The Finite-Difference Time-Domain Method, Artech House, Boston, London.

  26. Rimer, M. and Martinez, T., (2004) Softprop: softmax neural network backpropagation learning, IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541), Budapest, 2, pp. 979-983.

  27. Le Cun, Y. and Bengio, Y., (1995) Convolutional networks for images, speech, and time series, in: Michael A. Arbib. (eds.), The Handbook of Brain Theory and Neural Networks, Cambridge, pp. 255-258.


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