Abstract
In the state estimation problem for nonlinear systems, the Unscented Kalman Filter (UKF) has gained attention as an algorithm capable of accurate state estimation based on high-fidelity discretization for strongly nonlinear systems. Furthermore, for applying the UKF to continuous-time state-space models, a method employing the Runge-Kutta method in the time-update equation for sigma points has already been proposed to achieve high-precision state estimation. While this method uses high-order numerical approximations, the associated decrease in computational efficiency due to processing time becomes problematic. It is thus unsuitable for the state estimation of relatively fast-moving objects, such as autonomous vehicles and drones, which require high sampling frequencies. In this study, to reduce computational load while achieving relatively high estimation accuracy, we newly apply the Adams-Bashforth method to the UKF algorithm. The effectiveness of the proposed method is demonstrated by first explaining a low-dimensional model's state estimation problem, followed by a comparison of estimation accuracy and computation time in state estimation simulations for the UAV model of an Osprey-type drone.