Sensor fusion of delay and non-delay signal using ensemble Kalman filter with moving covariance

This paper describes the design of ensemble Kalman filter (EnKF) to implement fusion of the delay and non-delay data for nonlinear discrete-time system in order to achieve the excellent dynamic response. We proposed a fusion method with EnKF that only needs to update the stored covariance between two different time instants, instead of classical method, which is re-performing Kalman operation at every step from the time of measured delay signal to current time. To solve the fusion method, the EnKF algorithm is modified to obtain members of measurement ensemble from uncorrelated sensors in the system but not a Monte Carlo method. With less computational cost comparing to the classical method and the uniformity of the computation in every iteration, the EnKF is superior to extended Kalman filter (EKF) and offer much advantage in terms of estimation performance, which is verified by using MATLAB simulation on the high-update rate Wheel Mobile Robot (WMR).