Development of a hybrid neural network model for mine detection by using ultrawideband radar data

Authors

DOI:

https://doi.org/10.15587/1729-4061.2023.279891

Keywords:

multilayer perceptron-filter, Hilbert block, oscillatory neural network, resonance

Abstract

The object of the study is the architecture of a hybrid neural network for mine recognition using ultra-wideband radar data. The work solves the problem of filtering reflected signals with interference and recognizing mines detected by ultra-wideband (UWB) radar. A hybrid neural network model in combination with the Adam learning algorithm is proposed. Filtering of reflected signals from mines is carried out using an MLP (multilayer perceptron) filter, which selects low-amplitude parts of signals that carry information about a hidden mine from the entire reflected signal. Mine recognition is carried out by a Hilbert block and an oscillatory neural network, which are included in the structure of a hybrid neural network. The peculiarity of the obtained results, which allowed to solve the investigated problem, is the transformation of the signal frequency by the Hilbert block and the recognition of mines by the oscillatory neural network in the resonant mode. The three-layer MLP filter effectively filters out the unwanted component in the total signal reflected from the subsurface object, as the MSE (Mean Squared Error) of the MLP filter is 1·10-5. If the frequency of the Hilbert signal is equal to the natural frequency of oscillations of neurons  then the recognition of signals with a small amplitude from subsurface objects is carried out by an oscillatory neural network based on the resonant amplitude, which is indicated by a small value of cross-entropy. The proposed model of a hybrid neural network provides amplification of useful signals due to resonance and has higher performance compared to existing models of artificial neural networks. The practical significance of the obtained results lies in their application in the field of automated neural network technologies for detection and recognition of subsurface objects of various nature based on reflected radar signals with an amplitude at the noise level

Author Biographies

Vasyl Lytvyn, Lviv Polytechnic National University

Doctor of Technical Sciences, Professor

Department of Information Systems and Networks

Ivan Peleshchak, Lviv Polytechnic National University

PhD, Assistant

Department of Information Systems and Networks

Roman Peleshchak, Lviv Polytechnic National University

Doctor of Physical and Mathematical Sciences, Professor

Department of Information Systems and Networks

Oleksandr Mediakov, Lviv Polytechnic National University

Department of Information Systems and Networks

Petro Pukach, Lviv Polytechnic National University

Doctor of Technical Sciences, Professor

Department of Computational Mathematics and Programming

References

  1. Daniels, D. J. (2004). Ground penetrating radar. London: IEEE. doi: https://doi.org/10.1049/pbra015e
  2. Harmuth, H. (1981). Nonsinusoidal waves for radar and radiocommunications. New York: Academic Press, 404.
  3. Taylor, J. D. (2012). Ultrawideband Radar: applications and design. Boca Raton: CRC Press. doi: https://doi.org/10.1201/b12356
  4. Ristic, A., Govedarica, M., Vrtunski, M., Pctrovacki, D. (2014). Application of GPR for creating underground structure model of specific areas of interest. Proceedings of the 15th International Conference on Ground Penetrating Radar. Brussels, 450–455. doi: https://doi.org/10.1109/icgpr.2014.6970464
  5. Liu, H., Huang, X., Xing, B., Cui, J., Spencer, B. F., uo Liu, Q. H. (2018). Estimating Azimuth of Subsurface Linear Targets By Polarimetric GPR. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium. Valencia, 6784–6787. doi: https://doi.org/10.1109/igarss.2018.8517637
  6. Zhao, S., Al-Qadi, I. L. (2019). Super-Resolution of 3-D GPR Signals to Estimate Thin Asphalt Overlay Thickness Using the XCMP Method. IEEE Transactions on Geoscience and Remote Sensing, 57 (2), 893–901. doi: https://doi.org/10.1109/tgrs.2018.2862627
  7. Taflove, A., Hagness, S. (2005). Computational Electrodynamics: The Finite-Difference Time-Domain Method. London, Boston: Artech House, 629–670. doi: https://doi.org/10.1016/b978-012170960-0/50046-3
  8. Liu, Y., Guo, L. X. (2016). FDTD investigation on GPR detecting of underground subsurface layers and buried objects. Proceedings of the IEEE MTT-S International Conference on Numerical Electromagnetic and Multiphysics Modeling and Optimization. Beijing. doi: https://doi.org/10.1109/nemo.2016.7561622
  9. Giannakis, I., Giannopoulos, A., Warren, C. (2019). A Machine Learning-Based Fast-Forward Solver for Ground Penetrating Radar With Application to Full-Waveform Inversion. IEEE Transactions on Geoscience and Remote Sensing, 57 (7), 4417–4426. doi: https://doi.org/10.1109/tgrs.2019.2891206
  10. Earp, S. L., Hughes, E. S., Elkins, T. J., Vickers, R. (1996). Ultra-wideband ground-penetrating radar for the detection of buried metallic mines. Proceedings of the 1996 IEEE National Radar Conference. Ann Arbor, 7–12. doi: https://doi.org/10.1109/nrc.1996.510648
  11. Millot, P., Castanet, L., Casadebaig, L., Maaref, N., Gaugue, A., Menard, M. et al. (2015). An UWB Through-The-Wall radar with 3D imaging, detection and tracking capabilities. Proceedings of the European Radar Conference (EuRAD). Paris, 237–240. doi: https://doi.org/10.1109/eurad.2015.7346281
  12. Hai-zhong, Y., Yu-feng, O., Hong, C. (2012). Application of ground penetrating radar to inspect the metro tunnel. Proceedings of the 14th International Conference on Ground Penetrating Radar (GPR). Shanghai, 759–763. doi: https://doi.org/10.1109/icgpr.2012.6254963
  13. Holbling, Z., Mihaldinec, H., Ambrus, D., Dzapo, H., Bilas, V., Vasic, D. (2017). UWB localization for discrimination-enabled metal detectors in humanitarian demining. In Proceedings of the IEEE Sensors Applications Symposium (SAS). Glassboro, 1–4. doi: https://doi.org/10.1109/sas.2017.7894073
  14. Morgenthaler, A., Rappaport, C. (2013). Fast GPR underground shape anomaly detection using the Semi-Analytic Mode Matching (SAMM) algorithm. Proceedings of the IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Melbourne, 1422–1425. doi: https://doi.org/10.1109/igarss.2013.6723051
  15. Li, W., Zhou, H., Wan, X. (2012). Generalized Hough Transform and ANN for subsurface cylindrical object location and parameters inversion from GPR data. Proceedings of the 14th International Conference on Ground Penetrating Radar (GPR). Shanghai, 281–285. doi: https://doi.org/10.1109/icgpr.2012.6254874
  16. Dumin, O., Plakhtii, V., Pryshchenko, O., Pochanin, G. (2020). Comparison of ANN and Cross-Correlation Approaches for Ultra Short Pulse Subsurface Survey. Proceedings of the 15th International Conference on Advanced Trends in Radioelectronics, Tel-ecommunications and Computer Engineering (TCSET – 2020). Lviv-Slavske, 1–6. doi: https://doi.org/10.1109/tcset49122.2020.235459
  17. Sharma, P., Kumar, B., Singh, D., Gaba, S. P. (2016). Metallic Pipe Detection using SF GPR: A New Approach using Neural Network. Proceedings of the 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Beijing, 6609–6612. doi: https://doi.org/10.1109/igarss.2016.7730726
  18. Dumin, O. M., Pryshchenko, O. A., Plakhtii, V. A., Pochanin, G. P. (2020). Detection and classification of landmines using UWB antenna system and ANN analysis. Visnyk of V.N. Karazin Kharkiv National University, Series “Radio Physics and Electronics”, 33, 7–19. doi: https://doi.org/10.26565/2311-0872-2020-33-01
  19. Bralich, J., Reichman, D., Collins, L. M., Malof, J. M. (2017). Improving convolutional neural networks for buried target detection in ground penetrating radar using transfer learning via pretraining. Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XXII, Vol. 10182. International Society for Optics and Photonics. SPIE, 198–208. doi: https://doi.org/10.1117/12.2263112
  20. Lameri, S., Lombardi, F., Bestagini, P., Lualdi, M., Tubaro, S. (2017). Landmine detection from GPR data using convolutional neural networks. Proceedings of the 2017 25th European Signal Processing Conference (EUSIPCO). Kos, 508–512. doi: https://doi.org/10.23919/eusipco.2017.8081259
  21. Pochanin, G. P., Capineri, L., Bechtel, T. D., Falorni, P., Borgioli, G., Ruban, V. P. et al. (2020). Measurement of Coordinates for a Cylindrical Target Using Times of Flight from a 1-Transmitter and 4-Receiver UWB Antenna System. IEEE Transactions on Geoscience and Remote Sensing, 58 (2), 1363–1372. doi: https://doi.org/10.1109/tgrs.2019.2946064
  22. Dumin, O. M., Plakhtii, V. A., Prishchenko, O. A., Shyrokorad, D. V., Volvach, I. S. (2019). Influence of denoising of input signal on classification of object location by artificial neural network in ultrawideband radiointroscopy. Visnyk of V.N. Karazin Kharkiv National University, Series “Radio Physics and Electronics”, 31, 27–35. doi: https://doi.org/10.26565/2311-0872-2019-31-03
  23. Peleshchak, R., Lytvyn, V., Bihun, O., Peleshchak, I. (2019). Structural Transformations of Incoming Signal by a Single Nonlinear Oscillatory Neuron or by an Artificial Nonlinear Neural Network. International Journal of Intelligent Systems and Applications, 11 (8), 1–10. doi: https://doi.org/10.5815/ijisa.2019.08.01
  24. Lytvyn, V., Vysotska, V., Peleshchak, I., Rishnyak, I., Peleshchak, R. (2018). Time Dependence of the Output Signal Morphology for Nonlinear Oscillator Neuron Based on Van der Pol Model. International Journal of Intelligent Systems and Applications, 10 (4), 8–17. doi: https://doi.org/10.5815/ijisa.2018.04.02
  25. Janson, N. B., Pavlov, A. N., Neiman, A. B., Anishchenko, V. S. (1998). Reconstruction of dynamical and geometrical properties of chaotic attractors from threshold-crossing interspike intervals. Physical Review E, 58 (1), R4–R7. doi: https://doi.org/10.1103/physreve.58.r4
  26. Kingma, D. P., Ba, J. (2015). Adam: a method for stochastic optimization. Proceedings of the 3rd International Conference on Learning Representations (ICLR 2015). San Diego, 1–15. doi: https://doi.org/10.48550/arXiv.1412.6980
  27. Abbasi, A., Javed, A. R., Iqbal, F., Kryvinska, N., Jalil, Z. (2022). Deep learning for religious and continent-based toxic content detection and classification. Scientific Reports, 12 (1). doi: https://doi.org/10.1038/s41598-022-22523-3
  28. Bashir, M. F., Arshad, H., Javed, A. R., Kryvinska, N., Band, S. S. (2021). Subjective Answers Evaluation Using Machine Learning and Natural Language Processing. IEEE Access, 9, 158972–158983. doi: https://doi.org/10.1109/access.2021.3130902
Development of a hybrid neural network model for mine detection by using ultrawideband radar data

Downloads

Published

2023-06-30

How to Cite

Lytvyn, V., Peleshchak, I., Peleshchak, R., Mediakov, O., & Pukach, P. (2023). Development of a hybrid neural network model for mine detection by using ultrawideband radar data. Eastern-European Journal of Enterprise Technologies, 3(9 (123), 78–85. https://doi.org/10.15587/1729-4061.2023.279891

Issue

Vol. 3 No. 9 (123) (2023): Information and controlling system

Section

Information and controlling system

License

Copyright (c) 2023 Vasyl Lytvyn, Ivan Peleshchak, Roman Peleshchak, Oleksandr Mediakov, Petro Pukach

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

The consolidation and conditions for the transfer of copyright (identification of authorship) is carried out in the License Agreement. In particular, the authors reserve the right to the authorship of their manuscript and transfer the first publication of this work to the journal under the terms of the Creative Commons CC BY license. At the same time, they have the right to conclude on their own additional agreements concerning the non-exclusive distribution of the work in the form in which it was published by this journal, but provided that the link to the first publication of the article in this journal is preserved.

A license agreement is a document in which the author warrants that he/she owns all copyright for the work (manuscript, article, etc.).
The authors, signing the License Agreement with TECHNOLOGY CENTER PC, have all rights to the further use of their work, provided that they link to our edition in which the work was published.
According to the terms of the License Agreement, the Publisher TECHNOLOGY CENTER PC does not take away your copyrights and receives permission from the authors to use and dissemination of the publication through the world's scientific resources (own electronic resources, scientometric databases, repositories, libraries, etc.).
In the absence of a signed License Agreement or in the absence of this agreement of identifiers allowing to identify the identity of the author, the editors have no right to work with the manuscript.
It is important to remember that there is another type of agreement between authors and publishers – when copyright is transferred from the authors to the publisher. In this case, the authors lose ownership of their work and may not use it in any way.