Development of the method of complexing the results of radio monitoring and remote earth sensing

Authors

DOI:

https://doi.org/10.15587/1729-4061.2022.266276

Keywords:

complex monitoring, monitoring objects, a priori uncertainty, remote sensing of the Earth, unmanned aerial vehicles

Abstract

The objects of the research are the objects of monitoring of groups of troops (forces). The relevance of the research lies in the need for a comprehensive analysis of monitoring objects from several sources of information. The results of the analysis show that the most reliable and accurate information comes from aerial monitoring, orbital remote sensing of the Earth and radio monitoring. At the same time, instrumental errors of radio monitoring devices do not allow determining the location of sources of radio radiation with the accuracy necessary for localization (neutralization) of threats. A method of integrating the results of radio monitoring and remote sensing of the Earth has been developed. The essence of the proposed research is the complex processing of monitoring results from various sources of information extraction. The difference between the proposed method and the known ones is that the specified method contains the following improved procedures:

‒ taking into account the type of uncertainty about the state of the monitoring object (complete uncertainty, partial uncertainty, full awareness);

‒ carry out a multi-level analysis of the state of the monitoring object according to 4 levels and 3 significant events;

‒ detection of a monitoring object as part of a group monitoring object.

The use of the proposed approach to radio monitoring information processing and monitoring using unmanned aerial vehicles/devices of remote sensing of the Earth allows to reduce the time required for deciphering aerospace images by at least 1.3 times. At the same time, the accuracy of determining the coordinates will be limited by the resolution of the equipment of unmanned aerial vehicles/ devices of remote sensing of the Earth and is of the order of 0.5 m.

Author Biographies

Maksym Rohovets, Korolyov Zhytomyr Military Institute

PhD, Head of Department

Department No. 11

Serhiy Hatsenko, The National University of Defense of Ukraine named after Ivan Chernyakhovskyi

PhD, Deputy Head

Department of Intelligent

Hryhorii Radzivilov, Military Institute of Telecommunications and Information Technologies named after Heroes of Kruty

PhD, Associate Professor, Deputy Head of Institute

Institute for Research

Yurii Pribyliev, The National University of Defense of Ukraine named after Ivan Chernyakhovskyi

Doctor of Technical Sciences, Associate Professor, Professor

Department of Information Technology Application and Information Security

Roman Vozniak, The National University of Defense of Ukraine named after Ivan Chernyakhovskyi

PhD, Deputy Head of Department

Department of Information Technology Application and Information Security

Institute for Providing Troops (Forces) and Information Technologies

Mykola Dorofeev, Military unit A3444

PhD, Leading Researcher, Leading Test Engineer

Scientific and Research Department of Tests of Missile and Artillery Weapons

Vitalii Yarovyi, Military Institute of Telecommunications and Information Technologies named after Heroes of Kruty

Adjunct

Oleh Hrebeniuk, Korolyov Zhytomyr Military Institute

Adjunct

Dmitry Picus, Military Academy (Odesa)

Senior Lecturer

Department of Operations Management of Military Intelligence and Special Operations Units

Yurii Ryndin, Military Academy (Odesa)

Senior Lecturer

Department of Operations Management of Military Intelligence and Special Operations Units

References

  1. Bashkyrov, O. M., Kostyna, O. M., Shyshatskyi, A. V., Viliukha, Yu. I. (2015). Rozvytok intehrovanykh system zv’iazku ta peredachi danykh dlia potreb Zbroinykh Syl. Ozbroiennia ta viiskova tekhnika, 1 (5), 35–40.
  2. Dudnyk, V., Sinenko, Y., Matsyk, M., Demchenko, Y., Zhyvotovskyi, R., Repilo, I. et. al. (2020). Development of a method for training artificial neural networks for intelligent decision support systems. Eastern-European Journal of Enterprise Technologies, 3 (2 (105)), 37–47. doi: https://doi.org/10.15587/1729-4061.2020.203301
  3. Sova, O., Shyshatskyi, A., Salnikova, O., Zhuk, O., Trotsko, O., Hrokholskyi, Y. (2021). Development of a method for assessment and forecasting of the radio electronic environment. EUREKA: Physics and Engineering, 4, 30–40. doi: https://doi.org/10.21303/2461-4262.2021.001940
  4. Pievtsov, H., Turinskyi, O., Zhyvotovskyi, R., Sova, O., Zvieriev, O., Lanetskii, B., Shyshatskyi, A. (2020). Development of an advanced method of finding solutions for neuro-fuzzy expert systems of analysis of the radioelectronic situation. EUREKA: Physics and Engineering, 4, 78–89. doi: https://doi.org/10.21303/2461-4262.2020.001353
  5. Alpeeva, E. A., Volkova, I. I. (2019). The use of fuzzy cognitive maps in the development of an experimental model of automation of production accounting of material flows. Russian Journal of Industrial Economics, 12 (1), 97–106. doi: https://doi.org/10.17073/2072-1633-2019-1-97-106
  6. Zagranovskaia, A. V., Eissner, Iu. N. (2017). Simulation scenarios of the economic situation based on fuzzy cognitive maps. Modern economics: problems and solutions, 10 (94), 33‒47. doi: https://doi.org/10.17308/meps.2017.10/1754
  7. Simankov, V. S., Putiato, M. M. (2013). Issledovanie metodov kognitivnogo analiza. Sistemnyi analiz, upravlenie i obrabotka informatcii, 13, 31‒35.
  8. Ko, Y.-C., Fujita, H. (2019). An evidential analytics for buried information in big data samples: Case study of semiconductor manufacturing. Information Sciences, 486, 190–203. doi: https://doi.org/10.1016/j.ins.2019.01.079
  9. Ramaji, I. J., Memari, A. M. (2018). Interpretation of structural analytical models from the coordination view in building information models. Automation in Construction, 90, 117–133. doi: https://doi.org/10.1016/j.autcon.2018.02.025
  10. Pérez-González, C. J., Colebrook, M., Roda-García, J. L., Rosa-Remedios, C. B. (2019). Developing a data analytics platform to support decision making in emergency and security management. Expert Systems with Applications, 120, 167–184. doi: https://doi.org/10.1016/j.eswa.2018.11.023
  11. Zuiev, P., Zhyvotovskyi, R., Zvieriev, O., Hatsenko, S., Kuprii, V., Nakonechnyi, O. et. al. (2020). Development of complex methodology of processing heterogeneous data in intelligent decision support systems. Eastern-European Journal of Enterprise Technologies, 4 (9 (106)), 14–23. doi: https://doi.org/10.15587/1729-4061.2020.208554
  12. Yeromina, N., Kurban, V., Mykus, S., Peredrii, O., Voloshchenko, O., Kosenko, V. et. al. (2021). The Creation of the Database for Mobile Robots Navigation under the Conditions of Flexible Change of Flight Assignment. International Journal of Emerging Technology and Advanced Engineering, 11 (5), 37–44. doi: https://doi.org/10.46338/ijetae0521_05
  13. Chen, H. (2018). Evaluation of Personalized Service Level for Library Information Management Based on Fuzzy Analytic Hierarchy Process. Procedia Computer Science, 131, 952–958. doi: https://doi.org/10.1016/j.procs.2018.04.233
  14. Chan, H. K., Sun, X., Chung, S.-H. (2019). When should fuzzy analytic hierarchy process be used instead of analytic hierarchy process? Decision Support Systems, 125, 113114. doi: https://doi.org/10.1016/j.dss.2019.113114
  15. Osman, A. M. S. (2019). A novel big data analytics framework for smart cities. Future Generation Computer Systems, 91, 620–633. doi: https://doi.org/10.1016/j.future.2018.06.046
  16. Gödri, I., Kardos, C., Pfeiffer, A., Váncza, J. (2019). Data analytics-based decision support workflow for high-mix low-volume production systems. CIRP Annals, 68 (1), 471–474. doi: https://doi.org/10.1016/j.cirp.2019.04.001
  17. Harding, J. L. (2013). Data quality in the integration and analysis of data from multiple sources: some research challenges. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, XL-2/W1, 59–63. doi: https://doi.org/10.5194/isprsarchives-xl-2-w1-59-2013
  18. Kosko, B. (1986). Fuzzy cognitive maps. International Journal of Man-Machine Studies, 24 (1), 65–75. doi: https://doi.org/10.1016/s0020-7373(86)80040-2.
  19. Gorelova, G. V. (2013). Cognitive approach to simulation of large systems. Izvestiia IuFU. Tekhnicheskie nauki, 3, 239–250.
  20. Koshlan, A., Salnikova, O., Chekhovska, M., Zhyvotovskyi, R., Prokopenko, Y., Hurskyi, T. et. al. (2019). Development of an algorithm for complex processing of geospatial data in the special-purpose geoinformation system in conditions of diversity and uncertainty of data. Eastern-European Journal of Enterprise Technologies, 5 (9 (101)), 35–45. doi: https://doi.org/10.15587/1729-4061.2019.180197
  21. Rotshtein, A. P. (1999). Intellektualnye tekhnologii identifikatcii: nechetkie mnozhestva, geneticheskie algoritmy, neironnye seti. Vinnitca: “UNIVERSUM”, 320.
  22. Emelianov, V. V., Kureichik, V. V., Kureichik, V. M., Emelianov, V. V. (2003). Teoriia i praktika evoliutcionnogo modelirovaniia. Moskva: Fizmatlit, 432.
  23. Gorokhovatsky, V., Stiahlyk, N., Tsarevska, V. (2021). Combination method of accelerated metric data search in image classification problems. Advanced Information Systems, 5 (3), 5–12. doi: https://doi.org/10.20998/2522-9052.2021.3.01
  24. Levashenko, V., Liashenko, O., Kuchuk, H. (2020). Building Decision Support Systems based on Fuzzy Data. Advanced Information Systems, 4 (4), 48–56. doi: https://doi.org/10.20998/2522-9052.2020.4.07
  25. Meleshko, Y., Drieiev, O., Drieieva, H. (2020). Method of identification bot profiles based on neural networks in recommendation systems. Advanced Information Systems, 4 (2), 24–28. doi: https://doi.org/10.20998/2522-9052.2020.2.05
  26. Kuchuk, N., Merlak, V., Skorodelov, V. (2020). A method of reducing access time to poorly structured data. Advanced Information Systems, 4 (1), 97–102. doi: https://doi.org/10.20998/2522-9052.2020.1.14
  27. Shyshatskyi, A., Tiurnikov, M., Suhak, S., Bondar, O., Melnyk, A., Bokhno, T., Lyashenko, A. (2020). Method of assessment of the efficiency of the communication of operational troop grouping system. Advanced Information Systems, 4 (1), 107–112. doi: https://doi.org/10.20998/2522-9052.2020.1.16
  28. Raskin, L., Sira, O. (2016). Method of solving fuzzy problems of mathematical programming. Eastern-European Journal of Enterprise Technologies, 5 (4 (83)), 23–28. doi: https://doi.org/10.15587/1729-4061.2016.81292
  29. Lytvyn, V., Vysotska, V., Pukach, P., Brodyak, O., Ugryn, D. (2017). Development of a method for determining the keywords in the slavic language texts based on the technology of web mining. Eastern-European Journal of Enterprise Technologies, 2 (2 (86)), 14–23. doi: https://doi.org/10.15587/1729-4061.2017.98750
  30. Stepanenko, A., Oliinyk, A., Deineha, L., Zaiko, T. (2018). Development of the method for decomposition of superpositions of unknown pulsed signals using the second­order adaptive spectral analysis. Eastern-European Journal of Enterprise Technologies, 2 (9 (92)), 48–54. doi: https://doi.org/10.15587/1729-4061.2018.126578
  31. Gorbenko, I., Ponomar, V. (2017). Examining a possibility to use and the benefits of post-quantum algorithms dependent on the conditions of their application. Eastern-European Journal of Enterprise Technologies, 2 (9 (86)), 21–32. doi: https://doi.org/10.15587/1729-4061.2017.96321
  32. Lovska, A. A. (2015). Peculiarities of computer modeling of strength of body bearing construction of gondola car during transportation by ferry-bridge. Metallurgical and Mining Industry, 1, 49–54.
  33. Lovska, A., Fomin, O. (2020). A new fastener to ensure the reliability of a passenger car body on a train ferry. Acta Polytechnica, 60 (6), 478–485. doi: https://doi.org/10.14311/ap.2020.60.0478
  34. Shyshatskyi, A., Zvieriev, O., Salnikova, O., Demchenko, Ye., Trotsko, O., Neroznak, Ye. (2020). Complex Methods of Processing Different Data in Intellectual Systems for Decision Support System. International Journal of Advanced Trends in Computer Science and Engineering, 9 (4), 5583–5590. doi: https://doi.org/10.30534/ijatcse/2020/206942020
  35. Mahdi, Q. A., Shyshatskyi, A., Prokopenko, Y., Ivakhnenko, T., Kupriyenko, D., Golian, V. et. al. (2021). Development of estimation and forecasting method in intelligent decision support systems. Eastern-European Journal of Enterprise Technologies, 3 (9 (111)), 51–62. doi: https://doi.org/10.15587/1729-4061.2021.232718
Development of the method of complexing the results of radio monitoring and remote earth sensing

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Published

2022-10-30

How to Cite

Rohovets, M., Hatsenko, S., Radzivilov, H., Pribyliev, Y., Vozniak, R., Dorofeev, M., Yarovyi, V., Hrebeniuk, O., Picus, D., & Ryndin, Y. (2022). Development of the method of complexing the results of radio monitoring and remote earth sensing. Eastern-European Journal of Enterprise Technologies, 5(4(119), 16–23. https://doi.org/10.15587/1729-4061.2022.266276

Issue

Vol. 5 No. 4(119) (2022): Mathematics and Cybernetics - applied aspects

Section

Mathematics and Cybernetics - applied aspects

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Copyright (c) 2022 Maksym Rohovets, Serhiy Hatsenko, Hryhorii Radzivilov, Yurii Pribyliev, Roman Vozniak, Mykola Dorofeev, Vitalii Yarovyi, Oleh Hrebeniuk, Dmitry Picus, Yurii Ryndin

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