Sensor fusion algorithms for unmanned air vehicles

Abstract

Several sensor fusion algorithms for estimating the flight parameters of an unmanned air vehicle are presented. These include the classic linear Kalman filter and unscented Kalman filter. Two methods for improving the ability of the linear Kalman filter in estimating a nonlinear plant are proposed. The advantages and disadvantages of each algorithm are illustrated through simulation using a nonlinear six-degree-of-freedom model of the aircraft and simple sensor models.