Robust CI fusion Kalman filter under linearly correlated noise, uncertain noise variances and multiple networked-induced uncertainties

For systems with linearly correlated noise, uncertain noise variances, multiplicative noises, missing measurements, packets dropouts and one-step random measurement delay, the robust covariance intersection (CI) fusion steady-state Kalman filter is presented in this paper. The new model transformation method is presented to convert the original system into one with correlated noise and uncertain noise variances. According to the minimax robust filtering principle, three CI fusion steady-state robust Kalman filters are presented based on the worst-case system with the conservative upper bounds of uncertain noise variances. The extended Lyapunov equation approach with two generalized Lyapunov equations is presented to prove the robustness of local and fused steady-state robust filters in the sense that their actual filtering error variances are guaranteed to have the minimal upper bounds for all admissible uncertainties. The accuracy relations are proved. Finally, a simulation example is given to demonstrate effectiveness of the proposed results.