Robust weighted fusion Kalman estimators for ARMA signals with uncertain noise variances and missing measurements

The design problem of robust fusion Kalman estimators (predictor, filter, smoother) for multi-sensor multichannel autoregressive moving average (ARMA) signal system with uncertain noise variances and missing measurements is addressed. Applying the augmented state-space method and introducing fictitious noises, the ARMA signal system with missing measurements and uncertain noise variances is converted into the system with only uncertain noise variances. Based on the mini-max robust estimation principle, by the Lyapunov equation method, the robust weighted fusion Kalman estimators are designed in a unified framework. The corresponding minimal upper bound of the actual estimation error variances is given. An example is given to verify the correctness and effectiveness of the proposed results.