Robust Kalman Filtering for Discrete-Time Systems With Measurement Delay

This brief is concerned with robust Kalman filtering for linear discrete-time systems with both instantaneous and single delayed measurements. The norm-bounded parameter uncertainties enter into the system matrix of the state space model. A new approach through the re-organization of measurements is proposed to improve the efficiency of computation. A sufficient condition for the existence of a robust Kalman filter is derived. The performance is clearly demonstrated through analytical results and simulation experiments.