Sensor fusion for UAV navigation based on Derivative-free nonlinear Kalman Filtering

The paper studies a new nonlinear filtering method, the Derivative-free nonlinear Kalman filter and compares its performance to the one of other nonlinear estimators, in the problem of sensor fusion-based nonlinear control for trajectory tracking of unmanned aerial vehicles. The proposed filter is in accordance to basic concepts of differential flatness theory. The Derivative-free nonlinear Kalman Filter is compared against (i) Extended Kalman Filtering (EKF), (ii) Sigma-Point Kalman Filtering (SPKF), (iii) Particle Filtering (PF). It is shown that the Derivative-free nonlinear Kalman Filter is faster than the other nonlinear estimation algorithms while its accuracy of estimation is also quite satisfactory.