RUDN Journal of Engineering ResearchRUDN Journal of Engineering ResearchВестник Российского университета дружбы народов. Серия: Инженерные исследования2312-81432312-8151Peoples’ Friendship University of Russia named after Patrice Lumumba (RUDN University)4309510.22363/2312-8143-2024-25-4-427-440AWOHBRArticlesСтатьиResearch ArticleReal Time Estimation of the Wind Speed Components Based on Measurement Data from Satellite Navigationand Barometric MeasurementsИдентификация скорости ветра в режиме реального времени на основе данных спутниковой навигации и барометрических измеренийhttps://orcid.org/0000-0003-3926-10242472-6853KorsunOleg N.КорсунОлег Николаевич

D.Sc. (Technology), Head of the Scientific and Educational Center, State Scientific Research Institute of Aviation Systems (GosNIIAS); Professor, Department of Design and Certification of Aircraft Engineering, Moscow Aviation Institute (National Research University)

доктор технических наук, руководитель научно-образовательного центра, Государственный научно-исследовательский институт авиационных систем (ГосНИИАС); профессор кафедры проектирования и сертификации авиационной техники, Московский авиационный институт (Национальный исследовательский университет)

marmotto@rambler.ruhttps://orcid.org/0000-0002-7770-2962OmMoung HtangОмМоунг Хтанг

Cand. Sc. (Technology), Post-doctoral Candidate, Department of Design and Certification of Aircraft Engineering

кандидат технических наук, докторант кафедры проектирования и сертификации авиационной техники

mounghtangom50@gmail.comState Scientific Research Institute of Aviation SystemsГосударственный научно-исследовательский институт авиационных системMoscow Aviation Institute (National Research University)Московский авиационный институт (Национальный исследовательский университет)15122024254VOL 25, NO4 (2024)ТОМ 25, №4 (2024)42744002032025Copyright ©; 2024, Korsun O.N., Om M.H.Copyright ©; 2024, Корсун О.Н., Ом М.Х.2024Korsun O.N., Om M.H.Корсун О.Н., Ом М.Х.https://creativecommons.org/licenses/by-nc/4.0/legalcodehttps://journals.rudn.ru/engineering-researches/article/view/43095

This research work introduces a robust methodology for estimating three components of wind speed by leveraging airspeed, angle of attack, and sideslip angle measurements from both Satellite Navigation System (SNS) data and on-board sensors. By integrating these diverse sources of information, the proposed algorithm using parametric identification method achieves remarkable accuracy in determining the crucial parameters, i.e. wind speed components, necessary for flight operations. The research was conducted suggesting that the airflow has a constant direction and speed. The estimation of wind speed components is performed for distinct flight duration 20, 31 and 46 seconds in various types of flight maneuver. In order to determine the shortest duration of processing time at which the accurate estimates of three components of wind speed can be ensured, sliding window approach is applied. Notably, this approach yields reliable estimations within an impressive processing time interval of just 0.5 seconds. The findings have significant implications across various domains such as aviation safety enhancement, meteorology applications, and overall operational efficiency improvement of aircraft.

Настоящая исследовательская работа посвящена разработке надежного алгоритма оценивания трех проекций скорости ветра на основе измерений воздушной скорости, угла атаки и угла бокового скольжения как по данным спутниковой навигационной системы (СНС), так и по бортовым датчикам. Путем интеграции этих разнообразных источников информации, предложенный алгоритм, использующий метод параметрической идентификации, достигает значительной точности в определении важнейших параметров, то есть проекции скорости ветра, необходимых для выполнения полетов. Исследование проводилось в предположении, что направление и скорость ветра постоянны. Оценивание проекций скорости ветра производился для различных длительностей полета 20, 31 и 46 секунд при различных типах полетного маневра. Для определения наименьшего интервала времени обработки, при котором могут быть получены точные оценки трех проекций скорости ветра, применяется подход скользящего окна. Примечательно, что этот подход позволяет получать надежные оценки за впечатляющий интервал времени обработки, составляющий всего 0,5 секунды. Полученные результаты имеют важное значение для различных областей, таких как повышение безопасности полетов, применение в метеорологии и повышение общей эксплуатационной эффективности воздушных судов.

parametric identificationflight maneuverwind speedangle of attacksideslip angleairspeedпараметрическая идентификацияманевр полетаскорость ветраугол атакиугол скольжениявоздушная скоростьVasilchenko КК, Kochetkov YuА, Leonov VK, Pop-lavskii BК. Aircraft Flight Test. Moscow: Mashinostroenie: Publ.; 1996. (In Russ.)Byushgens GS, Chernyshev SL, Homan MG, Kuv-shinov VM, Fedosov EA. Aerodynamics, stability and controllability of supersonic aircraft. Moscow: Nauka Publ.; 2016. (In Russ.)Grumondz V. Airship Balancing and Stability at Longitudinal Established Motion. IOP Conference Series: Materials Science and Engineering. Moscow. 2019;476(1):012013. http://doi.org/10.1088/1757-899X/476/1/012013Luchtenburg DM, Rowley CM, Lohry MW, Marti-nelli L, Stengel RF. Unsteady high angle of attack aero-dynamic models of a generic jet transport. Journal of Aircraft. 2015;52(3):890-895. https://doi.org/10.2514/1.C032976Petoshin VI, Chasovnikov EA. Aerodynamic characterristics for a passenger aircraft model with harmonic oscillations on rolling and yawing angles at high angles of attack. Thermophysics and aeromechanics. 2013;20(1);39-48. https://doi.org/10.1134/S0869864313010046Grishina AY, Efremov AV. Development of a Controller Law for a Supersonic Transport Using Alternative Means of Automation in the Landing Phase. In: Streets DY, Korsun ON. (eds.). Recent Developments in High-Speed Transport. Springer Aerospace Technology. Springer, Singapore. 2023. https://doi.org/10.1007/978-981-19-9010-6_5Korsun ON, Poplavsky BK, Prihodko SJ. Intelligent support for aircraft flight test data processing in problem of engine thrust estimation. Procedia Comput. Sci. 2017;(103):82-87. https://doi.org/10.1016/j.procs.2017.01.017Korsun ON, Poplavsky BK, Om MH. Identification of the Engine Thrust Force Using Flight Test Data. In Pro-ceedings of the International Conference on Aerospace System Science and Engineering 2021. ICASSE 2021. Lecture Notes in Electrical Engineering; Jing Z, Strelets D. (eds.). Springer: Singapore, 2023;(849). https://doi.org/10.1007/978-981-16-8154-7_30Lin Z, Xiao H, Zhang X, Wang Z. Thrust Prediction of Aircraft Engine Enabled by Fusing Domain Knowledge and Neural Network Model. Aerospace. 2023;10(6):493. https://doi.org/10.3390/aerospace10060493Korsun ON, Nikolaev SV, Om MH. Detection of dynamic errors in aircraft flight data. In: Proceedings of the IOP Conference Series: Materials Science and Engineering, Moscow. 2021;(1027):012011. https://doi.org/10.1088/1757-899X/1027/1/012011Carlson HA, Verberg R, Hemati MS, Rowley CW. A flight simulator for agile fighter aircraft and nonlinear aerodynamics. Proceedings of the 53rd AIAA Aerospace Sciences Meeting, AIAA 2015-1506. Florida: Kissimmee, 2015. p. 1-22. https://doi.org/10.2514/6.2015-1506Nikolaev SV. Method of simulation in flight tests of aircraft. Applied Physics and Mathematics. 2017;(3):57-68. (In Russ.) EDN: XGIVELEfremov AV, Tjaglik MS, Irgaleev IH, Tsipenko VG. Integration of predictive display and aircraft flight control system. Proceeding of MATEC Web of Conferences. 2017;(99):03005. https://doi.org/10.1051/matecconf/20179903005Klein V. Estimation of Aircraft Aerodynamic Para-meters from Flight Data. Prog. Aerospace Sñi. 1989;(26):1-77.Klein V, Morelli E. Aircraft System Identification. Theory and Practice. Aeronautical Journal New Series. 2006;111(1123):602-603. https://doi.org/10.1017/S0001924000087194Korsun ON, Poplavsky BK. Approaches for flight tests aircraft parameter identification. 29th Congress of the International Council of the Aeronautical Sciences, ICAS. St. Petersburg; 2014:1804Jategaonkar RV. Flight vehicle system identification: A time domain methodology (USA, Reston: AIAA) 2006. https://doi.org/10.2514/4.866852Ovcharenko VN. Identification of aerodynamic characteristics of aircraft by flight data. Moscow: MAI Publishing House; 2017. (In Russ.)Larsson R. System Identification of Flight Mechanical Characteristics. Linköping Studies in Science and Tech-nology, Licentiate Thesis, Linköping University, Sweden. 2013. Available from: http://liu.diva-portal.org/smash/get/diva2:622859/FULLTEXT01.pdf (accessed: 11.04.2024)Om MH, Latt KZ. Influence Analysis of Input Signal Forms on the Accuracy of Aerodynamic Parameter Identi-fication in Aircraft Longitudinal Motion. Cloud of Science. 2017;4(4):636-649.Song Y, Song B, Seanor B., Napolitano M.R. On-line aircraft parameter identification using fourier transform regression with an application to F/A-18 HARV flight data. Journal of Mechanical Science and Technology. 2002;16(3):327-337. https://doi.org/10.1007/BF03185230Pushkov SG, Kharin EG, Kozhurin VR, Lovitsky LL. Technology for determining aerodynamic errors and air parameters in flight tests of aircraft using satellite means of trajectory measurements. Problems of flight safety. 2006;(7):12-19. (In Russ.)Pushkov SG, Lovitsky LL, Korsun ON. Wind speed determination methods in flight tests using satellite navigation systems. Мechatronics, automation, control. 2013;(9):65-70. (In Russ.) EDN: RBPNCVGuadaño LH, Valdés RA, Nieto FJ. Using aircraft as wind sensors for estimating accurate wind fields for air traffic management applications. Proceedings of the Institution of Mechanical Engineers, Part G: Journal of Aerospace Engineering. 2014;228(14). https://doi.org/10.1177/095441001452474Hurter Ch, Alligieri R, Gianazza D, Puechmorel S, Andrienko G. Wind parameters extraction from aircraft trajectories. Computers, Environment and Urban Systems. 2014. p. 1-16. https://doi.org/10.1016/j.compenvurbsys.2014.01.005 hal-00987690Benders S, Wenz A, Johansen TA. Adaptive Path Planning for Unmanned Aircraft Using In-flight Wind Velocity Estimation. 2018 International Conference on Unmanned Aircraft Systems (ICUAS). Dallas, TX, USA, 2018. p. 483-492. https://doi.org/10.1109/ICUAS.2018.8453341Khadeeja TK, Singh J. Wind Profile Estimation during Flight Path Reconstruction. Defence Science Journal. 2020;70(3):231-239. https://doi.org/10.14429/dsj.70.13596Hajiyev Ch, Cilden-Guler D, Hacizade U. Two-Stage Kalman Filter for Estimation of Wind Speed and UAV States by using GPS, IMU and Air Data System. Wseas transactions on electronics. 2019;10:60-65.Ahmed Z, Woolsey CA. Aerodynamic model-free wind estimation using a small, fixed-wing uncrewed aerial vehicle. Virginia Space Grant Consortium. 2024. Available from: https://vsgc.odu.edu/wp-content/uploads/2024/04/AhmedZakia_SRC_Submission__4_-1.pdf (accessed: 11.04.2024)