A Strong Tracking Unscented Kalman Filter Algorithm

Addressing the limitations of conventional Unscented Kalman Filter (UKF) in terms of a paucity of online adaptive tuning capability, which leads to low accuracy and poor robustness during system state estimation, this paper proposes an enhanced strong tracking UKF algorithm. This innovative approach necessitates only a single unscented transformation, thereby overcoming the computational overhead associated with existing strong tracking UKF algorithms that require two or even three transformations. Furthermore, the positioning and/or derivation of the fading factor in this algorithm differs significantly from other robust tracking UKF implementations. Finally, numerical simulation analysis validates the superior accuracy and robustness of the proposed algorithm.