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Evaluation of multiple model adaptive estimation of aircraft airspeed in close to real conditions

The paper discusses the problem of aircraft airspeed estimation from ground speed measurement affected by the unmeasurable wind speed. The estimation has to be obtained along any flight trajectory where linearized dynamics and the associated trim conditions are available. Under these assumptions one feasible approach to this estimation task is to apply multiple model (MM) estimation based on a bank of robust Kalman filters (RKFs). The unmeasurable wind effect can be taken into consideration by using an unknown input observer running simultaneously with the RKF. The paper focuses on the implementation issues and investigates the effect of state and measurement noises on the resulting state and unknown input (wind speed) estimates. The bank of steady state RKFs are designed assuming LTI dynamics at the given trim points in the minimum variance unbiased estimation (MVUE) framework. In the undertaken simulation experiments the aircraft is represented first by its LTI, then by its LPV model.