Multi-model robust weighted fusion steady-state Kalman estimators for systems with uncertain-variance multiplicative and additive white noises

摘要

For multi-model multisensor systems with uncertain-covariance multiplicative and additive white noises, a universal fictitious noise-based Lyapunov equation approach is presented, by which the original system can be converted into one with only uncertain additive noise variances. According to the minimax robust estimation principle, based on the worst-case system with conservative upper bounds of uncertain noise variances, a unified direct approach of designing the local and three weighted fusion steady-state Kalman estimators (predictor, filter, smoother) of the common state are presented. The three weighted fusers include fusers weighted by matrices, scalar and diagonal matrices. Their robustness is proved in the sense that their actual estimation error variances are guaranteed to have the corresponding minimal upper bounds for all admissible uncertainties. Their accuracy relations are also proved. A simulation example applied to uninterruptible power system (UPS) is given to verify the robustness and accuracy relations.