Robust CI fusion Kalman estimators for systems with multiplicative noises, colored measurements noises and uncertain noise variances

The robust covariance intersection (CI) fusion estimation problem for multisensor system with multiplicative noises, colored measurements noises and uncertain noise variances is addressed in this paper. By using augmented state approach, combining with a fictitious noise technique, the original system is converted into a multi- model system only with uncertain noise variances. The robust local Kalman filter and smoother are designed based on the robust local Kalman predictor. Then, based on the minimax robust estimation principle, robust CI fusion Kalman estimators (filter, predictor and smoother) are presented for worst-case system with the conservative upper bounds of uncertain noise variances. The robustness is proved by the Lyapunov equation approach. A simulation example applied to Internet-based three-tank system shows the correctness and effectiveness of the proposed results.