Robust fusion Kalman estimators for networked mixed uncertain systems with random one-step measurement delays, missing measurements, multiplicative noises and uncertain noise variances

Abstract For mixed uncertain multisensor networked systems simultaneously with four uncertainties including random one-step measurement delays, missing measurements, multiplicative noises and uncertain noise variances, three new approaches of solving robust fusion estimation problem are presented. They include augmented state approach with fictitious white noises, extended Lyapunov equation approach, and universal integrated covariance intersection (ICI) fusion approach. Applying them, the minimax robust local and five fused time-varying Kalman estimators (predictor, filter an smoother) are presented in the sense that their actual estimation error variances are guaranteed to have the corresponding minimal upper bounds for all admissible uncertainties. The five robust fusers include centralized fuser, fusers weighted respectively by matrices, diagonal matrices and scalars, and ICI fuser. Their robustness and accuracy relations are proved. The proposed approaches and results constitute an important methodology and a unified robust fusion Kalman filtering theory of solving the robust estimation problem. A simulation example applied to the vehicle suspension system shows their effectiveness and applicability.