Guaranteed Cost Robust Weighted Measurement Fusion Kalman Estimators With Uncertain Noise Variances and Missing Measurements

This paper is concerned with a guaranteed cost robust weighted measurement fusion (WMF) estimation problem for a multisensor system with both uncertain noise variances and missing measurements. By introducing the fictitious measurement white noises, the original multisensor system is converted into one only with uncertain noise variances. Two classes of guaranteed cost robust WMF Kalman estimators (predictor, filter, and smoother) are presented by the Lyapunov equation approach, based on the minimax robust estimation principle and the parameterization representation of uncertain noise variance perturbations. The maximal lower bound and minimal upper bound of actual accuracy deviations are given. A unified approach of designing the robust WMF Kalman estimators is presented based on the robust WMF Kalman predictor. A simulation example shows the correctness and effectiveness of the proposed results.