This paper studies robust state estimation problem in uncertain discrete stochastic system with measurement
missing and proposes a Kalman type recursive algorithm based on intermittent observation filtering algorithm and least square optimizing theory. The missing measurement model adopts Bernoulli random series based on given probability, which enables robust state estimation with intermittent observation stable for all possible missing measurement situation and admissible uncertainty. Finally, simulation and comparison results show the feasibility of the algorithm.