Sampled-data Suboptimal State Estimation over Lossy Networks

Abstract

This paper proposes a sampled-data state estimator which is suboptimal with respect to the error variance, over lossy networks. When signal loss is detected, the estimator switches gains and continues to update the estimate based upon recently received measurement. The gains are designed by means of a common solution of linear matrix inequalities.