Air Data Estimation Algorithm under Unknown Wind Based on Information Fusion

Abstract

Physical pressure sensors installed on a vehicle’s surface are the general way to find air data, such as true airspeed, attack angle, and sideslip angle. Under extreme flight conditions, failure of pressure measurements are a possibility. Estimating air data based only on navigation information and flight control parameters is a potential method for providing a backup virtual air data system (VADS). Ordinarily, wind velocity is assumed to be known in VADS. To solve the air data estimation problem without initial wind velocity, we propose air data estimation algorithms with and without wind models. We used kinematics equations and aerodynamic models to establish the relationship between navigation information and wind velocity. We estimated wind speed using nonlinear filtering algorithms, then obtained air data parameters. We ran simulation experiments with the proposed estimation algorithms, and the results show that the proposed method achieves higher convergence speed and estimation accuracy.