Design of unscented Kalman filter with noise statistic estimator

For the problem that the accuray of the conventional UKF declines and further diverges when the prior noise statistic is unknown or inaccurate, an unscented Kalman filter (UKF) with noise statistic estimator is designed. This UKF filtering algorithm based on maximum a posterior (MAP) estimation can estimate and correct the mean and covariance of the noise in real time while it estimates the states. The proposed UKF has the adaptive capability of dealing with variable noise statistic. The simulation results show the effectiveness of the proposed UKF filtering algorithm.