State estimation for a class of non-uniform sampling systems

The modeling and state estimation for a class of non-uniform sampling linear stochastic systems are studied, where the state is updated uniformly at a fast rate and the measurement is sampled non-uniformly once at most in a state update period. A state space model is developed to describe the dynamics at the measurement sampling points within a state update period. A non-augmented optimal state estimation algorithm is proposed in the linear minimum variance sense. Simulation results show the effectiveness of the algorithm.