Less computational unscented Kalman filter for practical state estimation of small scale unmanned helicopters

This paper presents the unscented Kalman filter (UKF) with reduced simplex sigma-point for the navigation system in a small scale unmanned helicopter. UKF is widely applied to nonlinear systems. However, the disadvantage of traditional UKF is the high computational cost caused by the unscented transformation step. The computational cost is proportional to the number of the constructed sigma-points. Therefore a reduced simplex sigma-point selection is proposed to be practically applied for the sensor fusion on the unmanned helicopter. The simulation and experimental results verify the computational load reduction.