Adaptive Cubature Kalman Filter Based on the Expectation-Maximization Algorithm

Abstract

A cubature Kalman filter is considered to be one of the most useful methods for nonlinear systems. However, when the statistical characteristics of noise are unknown, the estimation accuracy is degraded. Therefore, an adaptive square-root cubature Kalman filter (ASCKF) is designed to handle the unknown noise. The maximum likelihood criterion and expectation-maximization algorithm are employed to adaptively estimate the parameters of unknown noise, thus restraining the disturbance resulting from unknown noise and improving the estimation accuracy. The stability of the proposed algorithm is theoretically analyzed. Finally, simulations are carried out to illustrate that the performance of the ASCKF algorithm is much more reliable than that of a standard square-root cubature Kalman filter.