State Estimation for DES according to Partially Observed Stochastic Petri Nets

This paper concerns state estimation of stochastic discrete event systems. For that purpose, partially observed stochastic Petri nets are used to model the system and the sensors. From the proposed modelling and the collected measurements, timed sequences which are consistent with those measurements are obtained. Based on the events date, our approach consists on evaluating the probabilities of the marking trajectories using probabilistic model. Such probabilities are important since they reflect the most probable behavior of the system. State estimation is obtained as a consequence.