  This paper presents a new optimal filter namely past observation-based
extended Kalman filter for the problem of localization of Internet-based mobile
robot in which the control input and the feedback measurement suffer from
communication delay. The filter operates through two phases: the time update
and the data correction. The time update predicts the robot position by
reformulating the kinematics model to be non-memoryless. The correction step
corrects the prediction by extrapolating the delayed measurement to the present
and then incorporating it to the being estimate as there is no delay. The
optimality of the incorporation is ensured by the derivation of a multiplier
that reflects the relevance of past observations to the present. Simulations in
MATLAB and experiments in a real networked robot system confirm the validity of
the proposed approach.
