Guaranteed cost robust weighted measurement fusion steady-state Kalman predictors with uncertain noise variances

Abstract

Two classes of guaranteed cost robust weighted measurement fusion (WMF) one-step and multi-step Kalman predictors are presented by the Lyapunov equation approach for multisensor system with uncertain noise variances based on the minimax robust estimation principle. One class is to construct a maximal perturbation region of uncertain noise variances such that for all admissible perturbations in this region, the deviations of its actual accuracies with respect to the robust accuracy are guaranteed to remain within the prescribed range, and the maximal lower bound and minimal upper bound of accuracy deviations are given. The other class is to find minimal upper bound and maximal lower bound of accuracy deviations under given perturbation region of uncertain noise variances. The general and unified concept of guaranteed cost robustness is presented. The proof of the guaranteed cost robustness is presented by the Lyapunov equation approach. A simulation example shows the correctness and effectiveness of the proposed results.