Robust weighted measurement fusion steady-state Kalman predictor with uncertain parameters

In this paper, the problem of designing weighted measurement fusion robust steady-state Kalman predictors is considered for the linear discrete-time multisensor time-invariant system with the model parameter uncertainties. Using the minimax robust estimation principle, robust weighted measurement fusion and local steady-state Kalman predictors are presented by the approach of compensating the parameter uncertainties by a fictitious noise. Based on the Lyapunov equation approach, Its robustness is proved, the concept of the robust region is presented, and its robust accuracy is higher than those of the robust local Kalman predictors. A simulation example is presented to demonstrate how to search the robust region and show its good performance.