The purpose of this work is to optimize systematically the maneuver required to identify the wind and calibrate the airspeed sensor of a subsonic aircraft using a global positioning system method. The optimization is based on sensitivity analyses that require a considerable number of flight simulations. To face this challenging computational effort, we adapted and parallelized a particle swarm optimization algorithm. We also introduced a new formulation of the sensor model in the Bernstein form. The results show stability using the selected formulation and bring out nonobvious aliasing and precision loss effects that depend on the maneuver configuration. The knowledge of these effects allowed us to fine-tune the maneuver in order to improve the estimation's precision. Finally, we validated the method using the JSBSim flight simulator under calm and light turbulence conditions. (C) 2022 American Society of Civil Engineers.
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