Optimal estimator for a class of non-uniform sampling systems with missing measurements

The estimation problem for a class of non-uniform sampling linear stochastic systems with missing measurements is considered, where the state is updated uniformly and the sensor non-uniformly samples the measurement once at most in a state update period. By using innovation analysis approach, the optimal state estimators are designed at the state update points and measurement sampling points based on the received measurements. A simulation example is given to show the effectiveness of the algorithms.