Robust Centralized Fusion Steady-State Kalman Filter with Uncertain Parameters and Measurement Noise Variances

For the linear discrete time multisensor system with uncertain model parameters and measurement noise variances, the centralized fusion robust steady-state Kalman filter is presented by a new approach of compensating the parameter uncertainties by a fictitious noise. Based on the Lyapunov equation, it is proved that for given fictitious noise variance, the variances of the actual filtering errors have a less-conservative upper bound when the uncertainty of parameters is limited in a sufficiently small region which is called as robust region of the parameter uncertainties. Further, a simulation example demonstrates how to search the robust region. It is also proved that the robust accuracy of the centralized fusion robust steady-state Kalman filter is higher than that of each local robust Kalman filter. A simulation example shows its effectiveness.