A robust extended Kalman filter for discrete-time systems with uncertain dynamics, measurements and correlated noise

This paper presents a robust filter for discrete-time nonlinear systems subject to uncertainties. The nonlinear functions are assumed to be uncertain but belonging to a conic region. This condition is characterized as a Lipschitz condition on the system state and control signal residuals. The proposed design also allows dynamic and measurement noises to have unknown time-varying expected values, covariances and cross-covariances. The filter furnishes estimations with the a priori and a posteriori variance errors bounded for all allowed uncertainties.