State filtering with intermittent unknown input and retransmission delay

Abstract

When input information of a physical plant is transmitted to the remote state estimator via unreliable networks, this paper proposes a resilient Kalman filter able to recover the unbiased minimum variance state prediction after a minimum number of successive correct receptions. This goal is reached by encoding input information so that the decoded state model of the plant satisfies a generalized form of output-separability conditions originally used to solve a multiple faults detection and isolation problem.