Guaranteed cost robust weighted measurement fusion Kalman predictors for systems with moving average colored measurement noise, missing measurements and uncertain noise variances

This paper is concerned with the guaranteed cost robust weighted measurement fusion (WMF) prediction problem for systems with moving average colored measurement noise, missing measurements and uncertain noise variances. The under considered system can be converted into that only with uncertain noise variances by the application of rewriting moving average model of state space form, augmenting the original state model, and a fictitious noise technique. Then, two classes of guaranteed cost robust WMF Kalman predictors are presented based on the minimax robust estimation principle and parameterization representation of uncertain noise variances. The proposed guaranteed cost predictors can concurrently give maximal lower bound and minimal upper bound of accuracy deviations. The proof of the guaranteed cost robustness is proved by the Lyapunov equation approach. A simulation example shows the correctness and effectiveness of the proposed results.