Comparison of wind speed models within a Pitot-free airspeed estimation algorithm using light aviation data

This paper presents an analytical redundancy-based approach to the estimation of the true aircraft airspeed without the use of a Pitot probe. Measurements from an Inertial Measurement Unit (IMU), Global Positioning System (GPS), and angle of attack and sideslip angle vanes are used within a sensor fusion algorithm utilizing the kinematic model of a fixed-wing aircraft to co-estimate the airspeed, wind speed, and attitude of an aircraft. The presented model does not use information relative to the aircraft dynamic model, and therefore, is totally independent of the specific aircraft. Due to the necessity of the algorithm to determine the aircraft wind speed, a predictive model of the wind behaviour is necessary. This work compares two different stochastic wind models – the random walk (RW) and the Gauss–Markov (GM)–within the context of the airspeed estimation problem using flight data of a light aviation aircraft. The use of light aviation data is an important new consideration since previous work has focused on unmanned aircraft. The results of this study indicated that the RW model provides better performance over the GM model for airspeed estimation. Additionally, the light aviation data further reinforces the cross-platform capability of the considered algorithm and, furthermore, demonstrates the effectiveness of this method within manned flight.