基于量测一步预测信息的自调整UKF

针对无迹卡尔曼滤波(unscented Kalman filter, UKF)中自由调节参数的选取问题,通过研究不同的对于滤波性能的影响,提出基于量测一步预测信息的在线自调整的UKF方法。所提方法是通过根据每一滤波时刻量测的一步预测信息,对滤波参数进行选取,选出每一滤波时刻的最优滤波参数,从而实现算法的在线调整。数值仿真表明,基于量测一步预测信息的自调整UKF对于真实状态的跟踪效果要优于固定参数的无迹卡尔曼滤波。

Adaptive setting of scaling parameter of UKF based on step -prediction information of measurement

For the adjustable parameter selection problem of -κ- in the unscented Kalman filter(UKF), through the study of the impact of the different κ for filtering, the method based on the step prediction information of the measurement, which is an online adjustment of the UKF, is presented. Based on the prediction information of measurement in every filtering time, the filtering parameter is selected, which is optimal and can realize the on-line adjustment. Numerical simulations show that the adjustment UKF based on the step prediction information of the measurement tracks the real state better than the traditional UKF.