Weighted fusion robust steady-state Kalman filters for multisensor system with uncertain noise variances

A direct approach of designing weighted fusion robust steady-state kalman filters with uncertain noise variances is presented. based on the steady-state kalman filtering theory,using the minimax robust estimation principle and the unbiased linear minimum variance (ulmv) optimal estimation rule,the six robust weighted fusion steady-state kalman filters are designed based on the worst-case conservative system with the conservative upper bounds of noise variances. the actual filtering error variances of each fuser are guaranteed to have a minimal upper bound for all admissible uncertainties of noise variances. a lyapunov equation method for robustness analysis is proposed. their robust accuracy relations are proved. a simulation example verifies their robustness and accuracy relations. © 2014 wen-juan qi et al.