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Propozycje wdrożenia systemu wspomagania decyzji dla kontroli ognia obrony powietrznej na podstawie rozmytych sieci celów
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The purpose of the research was to improve the control of air defence firepower using fuzzy networks of target installations, enhancing the efficiency and accuracy of defensive actions. The research niche of this article is the optimization of decision support systems in air defence through the application of fuzzy logic to improve real-time threat assessment and response accuracy. The study hypothesized that the integration of fuzzy networks into air defence fire control would lead to improved decision-making accuracy and reduced response time under conditions of uncertainty. The methodology involved data collection using radar, acoustic, and infrared sensors; modelling of fuzzy systems with specialized software; the development of fuzzy rules for threat assessment; and the simulation of real combat conditions to evaluate system effectiveness and its integration with existing detection and tracking equipment. The results demonstrated that the proposed decision support system significantly enhances threat assessment accuracy, reduces reaction time, and improves overall air defence effectiveness. Simulation tests confirmed a notable increase in the speed and precision of defensive measures, highlighting the adaptability of the system to dynamic combat conditions. Furthermore, the integration of fuzzy networks with existing detection and tracking technologies facilitated rapid data processing and optimized firepower management, leading to cost reductions. The study contributes to the advancement of decision support methodologies in air defence by introducing an innovative approach based on fuzzy logic, which enhances the accuracy and efficiency of decision-making under conditions of operational uncertainty. Future research should focus on validating the system’s effectiveness in real-world deployments to further refine its performance.
Celem badania było usprawnienie kontroli siły ognia obrony powietrznej poprzez zastosowanie rozmytych sieci instalacji celów, co miało na celu zwiększenie efektywności i precyzji działań obronnych. Niszową problematyką poruszaną w artykule jest optymalizacja systemów wspomagania decyzji w obronie powietrznej, poprzez zastosowanie logiki rozmytej w celu poprawy oceny zagrożeń w czasie rzeczywistym i precyzji reakcji. W badaniu postawiono hipotezę, iż integracja sieci rozmytych z systemami kierowania ogniem obrony powietrznej prowadzi do zwiększenia dokładności podejmowania decyzji oraz skrócenia czasu reakcji w warunkach niepewności. Część badawcza obejmowała zbieranie danych za pomocą radarów, czujników akustycznych i podczerwieni; modelowanie systemów rozmytych przy użyciu specjalistycznego oprogramowania; opracowanie reguł rozmytych do oceny zagrożeń; oraz symulację rzeczywistych warunków bojowych w celu oceny skuteczności systemu oraz jego integracji z istniejącym sprzętem wykrywającym i śledzącym cele. Wyniki badań wykazały, iż zaproponowany system wspomagania decyzji znacząco zwiększa dokładność oceny zagrożeń, skraca czas reakcji oraz poprawia ogólną skuteczność obrony powietrznej. Testy symulacyjne potwierdziły znaczący wzrost szybkości i precyzji działań obronnych, podkreślając zdolność systemu do adaptacji do dynamicznych warunków bojowych. Ponadto integracja sieci rozmytych z istniejącymi technologiami wykrywania i śledzenia celów umożliwiła szybsze przetwarzanie danych i optymalizację zarządzania siłą ognia, co przyczyniło się do redukcji kosztów. Badanie wnosi wkład w rozwój metodologii wspomagania decyzji w obronie powietrznej poprzez wprowadzenie innowacyjnego podejścia opartego na logice rozmytej, które zwiększa dokładność i efektywność podejmowania decyzji w warunkach operacyjnej niepewności. Przyszłe badania powinny skupić się na walidacji skuteczności systemu w rzeczywistych warunkach operacyjnych w celu dalszego doskonalenia jego działania.
Czasopismo
Rocznik
Tom
Strony
211--228
Opis fizyczny
Bibliogr. 36 poz., rys., tab.
Twórcy
autor
- Department of Air Defense Forces Tactics of the Land Forces, Ivan Kozhedub Kharkiv National Air Force University, Ukraine
autor
- Department of Air Defense Forces Tactics of the Land Forces, Ivan Kozhedub Kharkiv National Air Force University, Ukraine
autor
- Department of Air Defense Forces Tactics of the Land Forces, Ivan Kozhedub Kharkiv National Air Force University, Ukraine
autor
- Department of Air Defense Forces Tactics of the Land Forces, Ivan Kozhedub Kharkiv National Air Force University, Ukraine
autor
- Department of Air Defense Forces Tactics of the Land Forces, Ivan Kozhedub Kharkiv National Air Force University, Ukraine
Bibliografia
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- [11] Hadi, H.J., Cao, Y., Nisa, K.U., Jamil, A.M., Ni, Q. 2023. A comprehensive survey on security, privacy issues and emerging defence technologies for UAVs. Journal of Network and Computer Applications, 213, 103607. https://doi.org/10.1016/j.jnca.2023.103607.
- [12] Hashimov, E., Khudeynatov, E. 2024. Methodology for assessing the effectiveness of the air defense system. Control, Navigation and Communication Systems, 1(75), 21-27. https://doi.org/10.26906/SUNZ.2024.1.021.
- [13] Horyń, W., Bielewicz, M., Joks, A. 2021. AI-supported decision-making process in multidomain military operations. In: A. Visvizi, M. Bodziany (Eds.), Artificial Intelligence and Its Contexts: Security, Business and Governance, 93-107. Cham: Springer. https://doi.org/10.1007/978-3-030-88972-2_7.
- [14] Hu, C., Wang, X., Li, M., Jiang, J. 2023. Navigating uncertainty in weapon system-of-systems planning: A hybrid multiobjective network-based optimization and fuzzy set approach. International Journal of Computational Intelligence Systems, 16, 136. https://doi.org/10.1007/s44196-023-00313-7.
- [15] Hu, J., Zhou, Q., McKeand, A., Xie, T., Choi, S.K. 2021. A model validation framework based on parameter calibration under aleatory and epistemic uncertainty. Structural and Multidisciplinary Optimization, 63, 645-660. https://doi.org/10.1007/s00158-020-02715-z.
- [16] Insaurralde, C.C., Blasch, E. 2022. Situation awareness decision support system for air traffic management using ontological reasoning. Journal of Aerospace Information Systems, 19(3), 224-245. https://doi.org/10.2514/1.I010989.
- [17] İşci, H., Günel, G.Ö. 2022. Fuzzy logic based air-to-air combat algorithm for unmanned air vehicles. International Journal of Dynamics and Control, 10(1), 230-242. https://doi.org/10.1007/s40435-021-00803-6.
- [18] Jałowiec, T., Pajka, A., Więckowski, B. 2023. Military logistics as a research field of management and quality sciences. Military Logistics Systems, 58(1), 149-160. https://doi.org/10.37055/slw/176011.
- [19] Ji, J., Zhou, W., Yu, M., Xu, Y., Sun, X., Zhu, K. 2021. Ontology construction and reasoning of air defense and anti-missile assistant decision based on distributed operation. In: 2021 7th International Conference on Big Data and Information Analytics, 157-165. Chongqing: Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/BigDIA53151.2021.9619638.
- [20] Kang, Y., Pu, Z., Liu, Z., Li, G., Niu, R., Yi, J. 2022. Air-to-air combat tactical decision method based on SIRMs fuzzy logic and improved genetic algorithm. In: L. Yan, H. Duan, X. Yu, (Eds.), Proceedings of 2020 International Conference “Advances in Guidance, Navigation and Control”, 3699-3709. Singapore: Springer. https://doi.org/10.1007/978-981-15-8155-7_308.
- [21] Kopczewski, M., Grobelny, Z., Świętochowski, N. 2023. Defense and deterrence as the foundation of the A2/AD system in smart city air defense. Safety & Defense, 9(1), 14-23. https://doi.org/10.37105/sd.198.
- [22] Lima Filho, G.M.D., Medeiros, F.L.L., Passaro, A. 2021. Decision support system for unmanned combat air vehicle in beyond visual range air combat based on artificial neural networks. Journal of Aerospace Technology and Management, 13, e3721. https://doi.org/10.1590/jatm.v13.1228.
- [23] Ranasinghe, K., Sabatini, R., Gardi, A., Bijjahalli, S., Kapoor, R., Fahey, T., Thangavel, K. 2022. Advances in Integrated System Health Management for mission-essential and safety-criticalaerospace applications. Progress in Aerospace Sciences, 128, 100758. https://doi.org/10.1016/j.paerosci.2021.100758.
- [24] Sánchez-Lozano, J.M., Correa-Rubio, J.C., Fernández-Martínez, M. 2022. A double fuzzy multi-criteria analysis to evaluate international high-performance aircrafts for defense purposes. Engineering Applications of Artificial Intelligence, 115, 105339. https://doi.org/10.1016/j.engappai.2022.105339.227.
- [25] Schell, T.L., Smart, R., Cefalu, M., Griffin, B.A., Morral, A.R. 2024. State policies regulating firearms and changes in firearm mortality. JAMA Network Open, 7(7), e2422948. https://doi.org/10.1001/jamanetworkopen.2024.22948.
- [26] Semenenko, O., Nozdrachov, O., Chernyshova, I., Melnychenko, A., Momot, D. 2024. Innovative technologies to improve energy efficiency and security of military facilities. Machinery & Energetics, 15(4), 147-156. https://doi.org/10.31548/machinery/4.2024.147.
- [27] Tuncer, O., Cirpan, H.A. 2022. Target priority based optimisation of radar resources for networked air defence systems. IET Radar, Sonar & Navigation, 16(7), 1212-1224. https://doi.org/10.1049/rsn2.12255.
- [28] Tuncer, O., Cirpan, H.A. 2023. Adaptive fuzzy based threat evaluation method for air and missile defense systems. Information Sciences, 643, 119191. https://doi.org/10.1016/j.ins.2023.119191.
- [29] Tytarenko, O., Vlasenko, E. 2024. Air defense in the Russian-Ukrainian war: Lessons and recommendations. Air Force of Ukraine, 1(6), 49-55. https://doi.org/10.33099/2786-7714-2024-1-6-49-55.
- [30] Volkov, A., Brechka, M., Stadnichenko, V., Yaroshchuk, V., Cherkashyn, S. 2023. The protection of critical infrastructure facilities from air strikes due to compatible use of various forces and means. Machinery & Energetics, 14(4), 23-32. https://doi.org/10.31548/machinery/4.2023.23.
- [31] Volkov, A.F., Lezik, O.V., Gorbachev, K.M., Basilo, S.M. 2019. Tactical art of Air Defense forces of the Ground Forces and its development based on the experience of modern armed conflicts. Collection of scientific papers of the Kharkiv National University of the Air Force, 4(62), 40-45. http://dx.doi.org/10.30748/zhups.2019.62.05.
- [32] Volkov, A.F., Stadnichenko, V.G. 2023. Directions of formalization of knowledge about the air situation in the existing and prospective automated control systems of air defense of the Ground Forces. Science and technology of the Air Force of the Armed Forces of Ukraine, 2(51), 7-14. https://doi.org/10.30748/nitps.2023.51.01.
- [33] Williams, I., Dahlgren, M. 2022. Boost-phase missile defense. https://missilethreat.csis.org/wp-content/uploads/2022/07/220624_Williams_BoostPhase_MissileDefense.pdf
- [34] Wu, Y., Kang, B., Wu, H. 2021. Strategies of attack-defense game for wireless sensor networks considering the effect of confidence level in fuzzy environment. Engineering Applications of Artificial Intelligence, 102, 104238. https://doi.org/10.1016/j.engappai.2021.104238.
- [35] Zhang, H., Wei, Y., Zhou, H., Huang, C. 2022. Maneuver decision-making for autonomous air combat based on FRE-PPO. Applied Sciences, 12(20), 10230. https://doi.org/10.3390/app122010230.
- [36] Zhao, R., Yang, F., Ji, L., Bai, Y. 2021. Dynamic air target threat assessment based on interval‐valued intuitionistic fuzzy sets, game theory, and evidential reasoning methodology. Mathematical Problems in Engineering, 2021(1), 6652706. https://doi.org/10.1155/2021/6652706.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-046a55ae-cd2a-44a5-a9c6-9088b5b04d64
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