An Improved Unscented Kalman Filter Based on STF for Nonlinear Systems

The advantages of recently developed Unscented Kalman Filter (UKF) for nonlinear systems are significant with its ease to implement, better accuracy and same order of computational complexity. However, UKF has as bad robustness as extended Kalman filter (EKF) on the modelling uncertainty, and is sensitive to the initial conditions. To overcome the limitations of UKF, an improved UKF based on the theory of Strong Tracking Filters (STF) is developed in the paper. The improved filter could adjust a filtering gain matrix on line by introducing a time-varied fading matrix. Its effectiveness is demonstrated using a simulation example of target tracking. The simulation results show that the improved UKF has good robustness and can rapidly converge.