Guaranteed Cost Robust Centralized Fusion Kalman Estimator for Systems with Moving Average Colored Measurement Noise, Missing Measurements and Uncertain Noise Variances

The guaranteed cost robust centralized fusion (CF) estimation problem for systems with uncertain noise variances, missing measurements and moving average colored measurement noise is addressed The original system is converted into one only with uncertain noise variances by model transformation method. The transformation is implemented by three procedures: converting moving average model to state space form, augmenting method and introducing fictitious noise technology to compensate multiplicative noise. Then, by parameterizing the perturbations of uncertain noise variances, two classes of guaranteed cost robust CF Kalman estimators are presented based on the minimax robust estimation principle for converted system only with uncertain noise variances. Two problems can be converted into optimisation problems with constraints, and they can be solved by Lagrange multiplier method and linear programme method. The proposed guaranteed cost estimators (predictor, filter and smoother) 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 and matrix decomposition. A simulation example used to UPS shows the correctness and effectiveness of the proposed results.