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Optimal Linear Estimators for Systems With Finite-Step Correlated Noises and Packet Dropout Compensations

This paper is concerned with the optimal estimation problem for discrete-time stochastic systems with finite-step auto- and cross-correlated noises and multiple packet dropouts induced by the unreliable networks. When a packet transmitted from the sensor to the data processing center is lost, its predictor is used as the compensation. The optimal linear estimators including filter, predictor and smoother that depend on the packet arriving rate are proposed in the linear minimum variance sense via an innovative analysis approach. They are computed in terms of the solutions of some auto- and cross-covariance matrices. A tracking system example is given to demonstrate the effectiveness of the proposed algorithms.