Optimal linear estimators for systems with multiple random measurement delays and packet dropouts

For linear discrete-time stochastic systems with multiple random measurement delays and packet dropouts, we give a new augmented method by introducing measurement outputs into the augmented state vector. Based on this augmented state vector, the optimal linear estimators including filter, predictor and smoother are developed in the linear minimum variance sense. They can reduce the computational burden compared with the augmented method in the existing literature. A simulation example verifies their effectiveness.