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The suitable aircraft performance monitoring(APM)parameters selection method can realize the efficient selection of domestic civil cruising APM parameters,which can provide the reliable data basis of aircraft performance analysis and calculation.The Sage-Husa noise estimator is introduced into unscented Kalman filter(UKF),and the adaptive unscented Kalman filter(AUKF)is constructed.AUKF is used to denoise the quick access recorder(QAR)data.The stable cruise parameter screening criteria are given,and the improved sliding time window algorithm is used to realize the stable cruise parameter selecting.The algorithm is verified by the sample data of the domestic ARJ21 aircraft.The results show that the adaptive unscented Kalman filter algorithm can improve the reliability of data,and the improved sliding time window algorithm can improve the selecting efficiency by about 50%.

Aircraft performance models play a key role in airline operations, especially in planning a fuel-efficient flight. In practice, manufacturers provide guidelines which are slightly modified throughout the aircraft life cycle via the tuning of a single factor, enabling better fuel predictions. However, this has limitations, in particular, they do not reflect the evolution of each feature impacting the aircraft performance. Our goal here is to overcome this limitation. The key contribution of the present article is to foster the use of machine learning to leverage the massive amounts of data continuously recorded during flights performed by an aircraft and provide models reflecting its actual and individual performance. We illustrate our approach by focusing on the estimation of the drag and lift coefficients from recorded flight data. As these coefficients are not directly recorded, we resort to aerodynamics approximations. As a safety check, we provide bounds to assess the accuracy of both the aerodynamics approximation and the statistical performance of our approach. We provide numerical results on a collection of machine learning algorithms. We report excellent accuracy on real-life data and exhibit empirical evidence to support our modeling, in coherence with aerodynamics principles.