METHOD FOR VERIFICATION OF ARTILLERY FIRING UNDER THE INFLUENCE OF RANDOM DISTURBANCES
DOI:
https://doi.org/10.32782/msd/2024.1/05Keywords:
artillery shot, random disturbances, information technology, ballistic wave, parabolic approximationAbstract
Random disturbances are always present in artillery firing. These disturbances cannot be eliminated during the preparation for firing. In practice, they are compensated for by consecutive shooting adjustment. Modern counter-battery tactics require minimizing the time of artillery units' firing exposure. Such tactics are the only way to preserve the combat capability and existence of the artillery units. In this regard, methods for verifying each artillery shot are very relevant. Verification is understood as confirming the effectiveness of the shot immediately after it is made. Verification as an assessment of the error in the coordinates of the projectile's burst can be carried out using optical or radar observation, or sound reconnaissance systems. However, the use of additional means is not always possible and effective. In this regard, verification methods based on the analysis of acoustic fields generated by firing are promising. Progressive information technologies are used for such analysis. A method for verifying the shot with the registration of the ballistic wave created by a projectile flying along a ballistic trajectory at supersonic speed is proposed. The signal of the ballistic wave is recorded by a distributed system of acoustic sensors located along the firing line. Based on the moments of arrival of the ballistic wave registered by the sensors, a system of approximating parabolas is constructed. The solution of the system allows for the determination of the expected point of the projectile's burst before it lands. The deviation of the burst point from the aiming point verifies the quality of the artillery shot. Simulation modeling of the proposed method has been carried out. It is demonstrated that parabolic approximation effectively compensates for random disturbances in firing. A comparison of the proposed method with the method of disturbance compensation by consecutive shooting adjustment has been conducted. It is shown that the proposed method significantly reduces the time of firing exposure of the weapon and the expenditure of projectiles to hit the target. The effectiveness of the verification method is confirmed by natural field testing.
References
Oprean, L.-G. (2020). Artillery from the perspective of firing effects and ensured capabilities. Scientific Bulletin Vol. XXV, No. 2(50) (pp. 107–113). DOI: 10.2478/bsaft-2020-0015.
John, Q.B. (2023). The More Things Change Russia’s War in Ukraine Mirrors the Past as Much as It Shows the Future. Military Review, July, (pр. 1–14). Retrieved from: https://www.armyupress.army.mil/Journals/Military-Review/Online-Exclusive/2023-OLE/The-More-Things-Change/.
Shevtsov, R. (2023). An improved mathematical model of fire damage to enemy artillery units by missile forces and artillery in operations. Social Development and Security, 13(1), pр. 13–22. Retrieved from: https://doi.org/10.33445/sds.2023.13.1.2.
Shim, Y., & Atkinson, M.P. (2018). Analysis of artillery shoot-and-scoot tactics. Naval Research Logistics (NRL), 65(3), 242–274. Retrieved from: https://doi:10.1002/nav.21803.
Linfang, Q., Guangsong, C., Minghao, T., & Jinsong, T. (2022). General design principle of artillery for firing accuracy.Defence Technology, Vol. 18. Is. 12, pp. 2125–2140. Retrieved from: https://doi.org/10.1016/j.dt.2022.09.001.
Mady, M., Khalil, M., & Yehia, M. (2020). Modelling and production of artillery firing-tables: case-study. Journal of Physics: Conference Series, pp. 1507–1515. Retrieved from: https://doi.org/10.1088/1742-6596/1507/8/082043.
Mostafa, K. (2022). Study on modeling and production inaccuracies forartillery firing. Archive of mechanical engineering, Vol. 69. No. 1, pp. 165–183. Retrieved from: https://doi.org/10.24425/ame.2021.139802.
Petlyuk, I., & Shchavinsky, Y. (2021). Use of simulation modeling systems for determination of appropriate characteristics of prospective artillery weapons. Collection of scientific works of Odesa Military Academy, № 14, pp. 11–22. Retrieved from: https://doi.org/10.37129/2313-7509.2020.14.1.11-22.
Sun, Y., Zhang, S., Lu, G., Zhao, J., Tian, J. and Xue, J., (2022). Research on a Simulation Algorithm for Artillery Firepower Assignment According to Region. 3rd International Conference on Computer Science and Management Technology (ICCSMT), Shanghai, pp. 353–356. Retrieved from: https://doi.org/10.1109/ICCSMT58129.2022.00082.
Maneva, N.S., Achkoskib, J.Z., Petreskic, D.T., Gocićd, M.L., & Rančiće, D.D. (2017). Smart field artillery information system: model development with an emphasis on collisions in single sign-onauthentication. Military Technical Courier, Vol. 65. Issue 2, pp. 442–463. Retrieved from: https://doi.org/10.5937/vojtehg65-12703.
Sviderok, S.M., Shabatura, U.V., & Prokopenko, A.O. (2016). Technique of the fire correction of artillery systems according to modern requiremernts to the data preparation for shooting. Military Technical Collection, № 14, pp. 99–103. Retrieved from: https://doi.org/ 10.33577/2312-4458.14.2016.99-103.
Swietochowski, N. (2019). Rules of artillery employment in combat operations. Scientific Journal of the Military University of Land Forces, vol. 51, N. 2(192), pp. 280–293. Retrieved from: https://doi.org/10.5604/01.3001.0013.2599.
Krzyzanowski, S. (2018). How to assess the accuracy of artillery fire. Scientific Journal of the Military University Of Land Forces, vol. 50, no. 1 (187), p. 25–39. Retrieved from: https://doi.org/10.5604/01.3001.0011.7355.
Zasoby pidgotovky ta upravlinnya vognem artylerii: monografiia / Petrenko V.M., Lyapa M.М., Prykhodko А.І. Sumy:SumDU [in Ukrainian].
Šilinger, K., Brabcová, K., & Potužák, L. (2019) Assessment of Possibility to Conduct Fire for Effect without Adjust Fire according to Observational Distance of a Target in Artillery Automated Fire Control Systems. International Journal of Electrical Engineering and Computer Science.1: vol. 59, no. 1(197), p. 125–139.
Boltenkov, V., Brunetkin, O., Dobrynin, Y., Maksymova, O., Kuzmenko, V., Gultsov, P., Demydenko, V., & Soloviova, O. (2021). Devising a method for improving the efficiency of artillery shooting based on the Markov model. Eastern-European Journal of Enterprise Technologies, vol. 6. N. 3(114), pp. 6–17. Retrieved from: https://doi.org/10.15587/1729-4061.2021.245854.
Washburn, A.R. (2002). Notes on Firing Theory. Naval Postgraduate School Monterey, California, 37 p. Retrieved from: https://www.researchgate.net/publication/238621088_Notes_on_Firing_Theory.
Bartulović, V., Trzun, Z., & Hoić, M. (2023). Use of Unmanned Aerial Vehicles in Support of Artillery Operations. Strategos, N. 7(1), рр. 71–92. Retrieved from: https://www.researchgate.net/publication/372657457_Use_of_Unmanned_Aerial_Vehicles_in_Support_of_Artillery_Operations.
Оprean, L.-G. (2023). Artillery and drone action issues in the war in Ukraine. Scientific Bulletin vol. 28, No. 1(55), pp. 73–78. Retrieved from: https://doi.org/10.2478/bsaft-2023-0008.
Khudov, H., Yuzova, I., Lisohorskyi, B., Solomonenko, Y., Mykus, S., Irkha, A., Onishchuk, V., Sukonko, S., Semiv, G., & Bondarenko, S. (2021). Development of methods for determining the coordinates of firing positions of roving mortars by a network of counter-battery radars. EUREKA: Physics and Engineering, N(3), pp. 140–150. Retrieved from: https://doi.org/10.21303/2461-4262.2021.001821.
Brzozowski, M., Pakowski, M., Nowakowski, M., Myszka, M. and Michalczewsk, M. (2019). Radars with the function of detecting and tracking artillery shells - selected methods of field testing. IEEE 5th International Workshop on Metrology for AeroSpace (MetroAeroSpace), Turin, Italy, pp. 429–434. DOI: 10.1109/MetroAeroSpace.2019.8869656.
Kochan, R. et al. (2019). Theoretical Error of Bearing Method in Artillery Sound Ranging. 10th IEEE International Conference on Intelligent Data Acquisition and Advanced Computing Systems: Technology and Applications (IDAACS), Metz, France, 2019, pp. 615–619. DOI: 10.1109/IDAACS.2019.8924450.
Damarla, T. (2015) Battlefield Acoustics. Springer International Publishing Switzerland. 262 p. Retrieved from: https://doi.org/10.1007/978-3-319-16036-8.
Dobrynin, Y., Volkov, V., Maksymov, M., & Boltenkov, V. (2020). Development of physical models for the formation of acoustic waves at artillery shots and study of the possibility of separate registration of waves of various types. Eastern-European Journal of Enterprise Technologies, 4(5 (106), 6–15. Retrieved from: https://doi.org/10.15587/1729-4061.2020.209847.
Dobrynin, Y.V., Boltenkov, V.O., & Maksymov, M.V. Information technology for automated assessment of the artillery barrels wear based on SVM classifier. (2020). Applied Aspects of Information Technology.; vol. 3, No. 3, pp. 117–132. Retrieved from: https://doi.org/10.15276/aait.03.2020.1.
Dobrynin, E., Maksymov, M. & Boltenkov, V. (2020) Development of a method for determining the wear of artillery barrels by acoustic fields of shots. Eastern–European Journal of Enterprise Technologies. 2020; 3(5) (105): 6–18. Retrieved from: https://doi.org/10.15587/1729-4061.2020.209847.
Zhuravlyov, O.O., Orlov, S.V., & Shulyakov, S.O. (2020). Matematychna model traektorii polyotu snaryada dalekobiynoi artyileriyskoi systemy. Systemy ozbroennia I viiskovatehnika. 62–68. Retrieved from: https://doi.org/10.30748/soivt.2020.63.09.
Calculation. Military Operations Research, 27(3), 77–94. Retrieved from: https://www.jstor.org/stable/27166357.
Corvo, A. (2022). Comment on projectile motion with quadratic drag using an inverse velocity expansion. Am. J. Phys. 90, pp. 861–864. Retrieved from: https://doi.org/10.1119/5.0097411.
Wessam, M.E., & Chen, Z.H. (2015). Firing Precision Evaluation For Unguided Artillery Projectile. Proceedings of the 2015 International Conference on Artificial Intelligence and Industrial Engineering., Atlantis Press. pp. 584–587. Retrieved from: https://doi.org/10.2991/aiie-15.2015.156.
STANAG 4355 (2022). The Lieske modified point mass and five degrees of freedom trajectory models – AOP-4355 EDITION A. (No enabled versions). Washington DC, United States: United States Department of Defense,. Retrieved from: https://publishers.standardstech.com/content/military-dod-stanag-4355. Accessed: 2023-11-14.
Aldoegre, M. (2019). Comparison between trajectory models for firing table application. North-West University, Potchefstroom. Retrieved from: https://5dok.net/document/7q08x49y-comparison-between-trajectorymodels-for-firing-table-application.html.
Le, O.B., Gervaise, C., & Mars, J.I. (2016). Time-Difference-of-Arrival Estimation Based on Cross Recurrence Plots, with Application to Underwater Acoustic Signals. Springer Proceedings in Physics, pp. 265–288. Retrieved from: https://hal.science/hal-01343668/document.
New Ukrainian art intelligence system «BARAK». Retrieved from: https://speka.media/v-ukrayini-testuyutnovu-sistemu-artrozvidki-barak-pozeyv.
«Hears» drones and missiles: how the FENEK system protects the infrastructure of Ukraine. Retrieved from: https://focus.ua/uk/digital/606501-slyshit-drony-i-rakety-kak-sistema-fenek-zashishaet-infrastrukturu-ukrainy-video.
