Optimal Kalman Filter for state estimation of a quadrotor UAV

In this work, the main objective is to study the Optimal Kalman Filtering (OKF) method for estimating the state vector of a small quadrotor UAV through incorporating the internal disturbances including the white Gaussian process and measurement noises. Firstly, the kinematic and dynamic model of the quadrotor is transformed into a discrete-time system via the linear extrapolation method. Secondly, for the sake of performing the high accuracy position and attitude tracking control of the quadrotor, the discrete-time flight controller is designed using second order discrete-time sliding mode technique. In addition, the estimation of the quadrotor aircraft's state vector is carried out with the use of OKF. The performance of the combination between the flight controller and the OKF is evaluated through simulation tests. Extensive simulation results show that the combined strategy has a good performance in terms of variance and state estimation.