Guaranteed Cost Robust Centralized Fusion Kalman Estimators with Uncertain Noise Variances and Missing Measurements

摘要

The guaranteed cost centralized fusion Kalman estimation problem for discrete-time systems with uncertain noise variances and missing measurements. Missing measurement is described as Bernoulli random variables. The system under consideration can be converted into one only with uncertain noise variances by the method of derandomness, then two classes of guaranteed cost robust centralized fusion Kalman estimators (filter, predictor and smoother) are presented by Lyapunov equation approach based on minimax robust estimation principle and parameterisation representation of uncertain noise variances. The proposed guaranteed cost estimators can concurrently give minimal upper and maximal lower bound of accuracy deviations. A unified approach of designing the robust centralized fusion Kalman estimators is presented based on the robust Kalman predictor. The proof of the guaranteed cost robustness is presented by the Lyapunov equation approach. A numerical simulation example shows the correctness and effectiveness of the proposed results.