An adaptive UKF for nonlinear state estimation via maximum likelihood principle

Abstract

For the purpose of improving the performance of unscented Kalman filter (UKF) under the condition without accurate system noise statistics, this paper presents a new adaptive UKF based on the maximum likelihood principle. According to the maximum likelihood principle, the estimation of system noise statistics is determined by minimizing the negative of log likelihood function of innovation sequences. Subsequently, the estimated system noise statistics are fed back to the standard UKF to overcome its limitation. The proposed adaptive UKF can enhance the adaptive capability of standard UKF through the online estimation of system noise statistics. The effectiveness and advantage of the proposed algorithm are verified by the numerical simulations and comparison analysis.