Robust Covariance Intersection Fusion Steady-State Kalman Filter with Uncertain Parameters

For the linear discrete time-invariant system with uncertain parameters and known noise variances, a robust covariance intersection (CI) fusion steady-state Kalman filter is presented by the new approach of compensating the parameter uncertainties by a fictitious noise. Based on the Lyapunov equation approach, it is proved that for the prescribed upper bound of the fictitious noise variances, there exists a sufficiently small region of uncertain parameters; such that its actual filtering error variances are guaranteed to have a less-conservative upper bound. This region is called the robust region. By the searching method, the robust region can be found. Its robust accuracy is higher than that of each local robust Kalman filter. A Monte-Carlo simulation example shows its effectiveness and the good performance.