Robust centralized fusion steady-state Kalman filter for multisensor uncertain systems

For the linear discrete time multisensor system with uncertain model parameters and 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 minimax robust estimation principle, a robust centralized fusion Kalman filter is presented based on the worst-case conservative systems with the conservative upper bounds of noise variances. It proves robustness by the Lyapunov approach. Its robust accuracy is higher than that of each local robust Kalman filter. A simulation example shows how to search the robust region of uncertain parameters and the good performance of the proposed robust Kalman filter.