Aircraft Parameter Identification in the Presence of Atmospheric Turbulence

Abstract

This paper investigates the method for identifying the unknown parameters in the dynamics of an aircraft from flight data affected by random disturbances due to wind gusts. Two general algorithms suitable for applying to such stochastic environments, i.e. the method of maximum likelihood (ML) estimation and the extended Kalman filter (EKF) technique, are examined for capability by numerical simulations. The advantages and shortcomings of each algorithm are discussed in detail, which leads to the conclusion that the combined use of the two algorithms provides a powerful on-line technique, insensitive to initial parameter estimates.