Robust Kalman Filtering Under Correlated Noise, Uncertain Variance Multiplicative Noise and Multiple Networked-Induced Uncertainties

By using the augmented method with fictitious noise and derandomization approach, the original system with uncertain noise variances, multiplicative noises, missing measurements, packets dropouts and one-step random measurement delays is converted into one only with uncertain fictitious noise variances. Then, based on the minimax robust filtering principle, the steady-state robust Kalman filter is designed for worst-case system with conversative upper bound of fictitious noise variances. The actual Kalman filtering error variance is guaranteed to have minimal upper bound for all admissible uncertainties. The robustness is proved by extended Lypunov equation method and matrix decomposition method. A simulation example used to tracking system shows that the correctness and effectiveness of the proposed results.