novtexmechМехатроника, автоматизация, управлениеMekhatronika, Avtomatizatsiya, Upravlenie1684-64272619-1253Commercial Publisher «New Technologies»10.17587/mau.24.590-597novtexmech-1453Research ArticleДИНАМИКА, БАЛЛИСТИКА И УПРАВЛЕНИЕ ДВИЖЕНИЕМ ЛЕТАТЕЛЬНЫХ АППАРАТОВDYNAMICS, BALLISTICS AND CONTROL OF AIRCRAFTСравнение подходов фильтрации Калмана при оценивании параметра движения самолетаA Comparison between Kalman Filtering Approaches in Aircraft Flight Signal EstimationКорсунО. Н.KorsunO. N.

д-р техн. наук, проф., нач. лаб.

г. Москва

Korsun Oleg N., Dr. Sc. (Eng.), Professor, Head of Laboratories

Moscow, 125319

Moscow, 125993

mamotto@rambler.ruГороСекуGoroSekou

аспирант

г. Москва

Postgraduate Student

Moscow, 125993

gorosekoi@gmail.comОмМоунг ХтангOmMoung Htang

канд. техн. наук, докторант

г. Москва

Ph. D., Doctoral candidate

Moscow, 125993

mounghtangom50@gmail.comГосударственный научно-исследовательский институт авиационных систем; Московский авиационный институт (Национальный исследовательский университет)РоссияState Research Institute of Aviation Systems; Moscow Aviation Institute (NRU)Russian FederationМосковский авиационный институт (Национальный исследовательский университет)РоссияMoscow Aviation Institute (NRU)Russian Federation2023011120232411590597Copyright © Commercial Publisher «New Technologies», 20232023Commercial Publisher «New Technologies»Commercial Publisher «New Technologies»https://mech.novtex.ru/jour/about/submissions#copyrightNoticehttps://mech.novtex.ru/jour/article/view/1453

В настоящее время требования к точности авиационных бортовых систем измерений постоянно повышаются, тогда как датчики имеют различные погрешности измерения сигналов, прежде всего случайные. Зашумленные сигналы, полученные из бортовых измерений, могут быть сглажены или отфильтрованы различными способами. Одним из наиболее популярных подходов является фильтрация Калмана, эффективность которой была доказана многими исследованиями.

В данной работе проводится сравнительный анализ расширенного фильтра Калмана и сигма-точечного, или взвешенного, фильтра Калмана, применяемых для оценивания угла тангажа самолета с использованием данных стендового моделирования. При моделировании водились также нормальные шумы измерений. По результатам, полученным в данной работе, можно отметить, что взвешенный фильтр Калмана работает лучше, когда априорные знания о шумах объекта и наблюдений достоверны. Однако эффективность взвешенного фильтра Калмана при оценке сигнала снижается, когда априорные знания о процессе становятся неопределенными, в то время как производительность расширенного фильтра Калмана остается стабильной. Это связано с тем, что взвешенный фильтр Калмана использует более сложные аппроксимации, которые характеризуются высокой чувствительностью к точности принятых допущений. Полученные результаты также показывают, что различные варианты калмановской фильтрации сохраняют актуальность по сравнению с распространившимися в последние годы методами сглаживания, основанными на идеях оптимального управления и эволюционных алгоритмах численной оптитмизации.

At present, the requirements for the accuracy of aircraft on-board measurement systems are constantly increasing, while sensors contain various errors in signal measurement, primarily random. Noisy signals from onboard measurements can be smoothed or filtered out in a variety of ways. One of the most popular approaches is Kalman filtering, the effectiveness of which has been proven by many studies. This paper presents a comparative analysis of the extended Kalman filter (EKF) and unscented Kalman filter (UKF), used to estimate the pitch angle of an aircraft using bench modeling data. During the simulation, the normal measurement noises are also generated. According to the results obtained in this paper, it can be noted that UKF performs better when a priori knowledge about the process noise is certain. However, the efficiency of UKF in estimating the signal deteriorates when a priori knowledge about the process becomes uncertain while the performance of EKF remains stable. This is due to the fact that UKF uses more sophisticated assumptions and therefore is more sensitive to these assumptions violation. The obtained results also show that various variants of Kalman filtering remain relevant in comparison with the smoothing methods that have spread in recent years, based on the ideas of optimal control and evolutionary algorithms for numerical optimization.

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The authors declare that there are no conflicts of interest present.