ABSTRACTThis work presents an alternative methodology for monitoring flight performance during airline operations using the available inboard instrumentation system. This method tries to reduce the disadvantages of the traditional specific range monitoring technique where instrumentation noise and cruise stabilisation conditions affect the quality of the performance monitoring results. The proposed method consists of using an unscented Kalman filter for aircraft performance identification using Newton’s flight dynamic equations in the body X, Y and Z axis. The use of the filtering technique reduces the effect of instrumentation and process noise, enhancing the reliability of the performance results. Besides the better quality of the monitoring process, using the proposed technique, additional results that are not possible to predict with the specific range method are identified during the filtering process. An example of these possible filtered results that show the advantages of this proposed methodology are the aircraft fuel flow offsets, as predicted in the specific range method, but also other important aircraft performance parameters as the aircraft lift and drag coefficients (CL and CD), sideslip angle (β) and wind speeds, giving the operator a deeper understanding of its aircraft operational status and the possibility to link the operational monitoring results to aircraft maintenance scheduling. This work brings a cruise stabilisation example where the selected performance monitoring parameters such as fuel flow factors, lift and drag bias, winds and sideslip angle are identified using only the inboard instrumentation such as the GPS/inertial sensors, a calibrated anemometric system and the angle-of-attack vanes relating each flight condition to a specific aircraft performance monitoring result. The results show that the proposed method captures the performance parameters by the use of the Kalman filter without the need of a strict stabilisation phase as it is recommended in the traditional specific range method, giving operators better flexibility when analysing and monitoring fleet performance.
New Method of Spatial Extrapolation of Meteorological Fields on the Mesoscale Level Using a Kalman Filter Algorithm for a Four-Dimensional Dynamic–Stochastic Model