Wind identification via Kalman filter for aircraft flow angles calibration

The calibration of air-data systems for the evaluation of the flow angles is based on the availability of high-accuracy reference measurements of angle-of-attack and angle-of-sideslip. Typically, these are obtained by auxiliary sensors directly providing the reference angles (e.g. nose-boom vanes) or by analytically reconstructing them using calibrated airspeed measurements and inertial data. With reference to this second methodology, this paper proposes a novel approach, in which the reference data for the flow angles calibration are obtained by using measurements coming from an inertial navigation system and an air data sensor. This is obtained by using the Kalman filter theory for the evaluation of the reference angle-of-attack and angle-of-sideslip. The methodology aims to lower the development costs of advanced aircraft. The designed Kalman Filter has been implemented in Matlab/Simulink and validated using flight data coming from two very different aircraft, the Piaggio Aerospace P1HH Medium Altitude Long Endurance (MALE) Unmanned Aerial System (UAS) and the Alenia-Aermacchi M346 ‘Master’ transonic trainer. This paper illustrates some results where the performance is evaluated by comparing the filter results with the data coming from high-accuracy nose-boom vanes.