An Adaptive UKF Algorithm and Its Application in Mobile Robot Control

Abstract

In order to improve the performance of the UKF a novel adaptive filter method is proposed. The error between the covariance matrices of innovation measurements and their corresponding estimations/predictions is utilized as the cost function. Based on the MIT rule, an adaptive algorithm is designed to online update the covariance of the process uncertainties by minimizing the cost function. The updated covariance is further fed back into the normal UKF. Such an adaptive mechanism is intended to compensate the lack on the priori knowledge of process uncertainty distribution and improve the performance of UKF for the applications such as active state and parameter estimations. Simulations are conducted with respect to the dynamics of an omni-directional mobile robot, and the results obtained by the proposed AUKF are compared with those by normal UKF to demonstrate the effectiveness and improvements.