Optimal Kalman filtering with random sensor delays, packet dropouts and missing measurements
In this paper an optimal Kalman filter design problem is studied for networked stochastic linear discrete-time systems with random measurement delays, packet dropouts and missing measurements. Any of these three uncertainties in the measurement can occur in the network in the same run. Based on a Markov chain, we develop a unified/combined model to accommodate random delay, packet dropouts and missing measurements. Some simulation examples are presented to show the effectiveness of the proposed approach.