Robust Weighted Fusion Kalman Estimators for Networked Multisensor Mixed Uncertain Systems With Random One-Step Sensor Delays, Uncertain-Variance Multiplicative, and Additive White Noises

Abstract

For networked multisensor systems with mixed uncertainties, including random one-step sensor delays, multiplicative noises and uncertain noise variances, a new augmented state approach with the fictitious noises is presented, by which the original system model is transformed into one without sensor delays and only with white noises. An extended Lyapunov equation approach with two Lyapunov equations is presented, which is applied to prove the robustness of the estimators. According to the minimax robust estimation principle, based on the worst-case system with conservative upper bounds of uncertain noise variances, the four weighted fusion robust time-varying and steady-state Kalman estimators (predictor, filter and 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. Their robustness and accuracy relations are proved, and their convergence in a realization is proved by the dynamic error system analysis (DESA) method. Specially, the presented modified robust covariance intersection (CI) fuser has higher robust accuracy than the original one. A simulation example applied to the uninterruptible power system (UPS) shows the effectiveness of the proposed approaches and results.