A particle filter-based data assimilation framework for discrete event simulations

With the advent of new sensor technologies and communication solutions, the availability of data for discrete event systems has greatly increased. This motivates research on data assimilation for discrete event simulations that has not yet fully matured. This paper presents a particle filter-based data assimilation framework for discrete event simulations. The framework is formally defined based on the Discrete Event System Specification formalism. To effectively apply particle filtering in discrete event simulations, we introduce an interpolation operation that considers the elapsed time (i.e., the time elapsed since the last state transition) when retrieving the model state (which was ignored in related work) in order to obtain updated state values. The data assimilation problem finally boils down to estimating the posterior distribution of a state trajectory with variable dimension. This seems to be problematic; however, it is proven that in practice we can safely apply the sequential importance sampling algorithm to update the random measure (i.e., a set of particles and their importance weights) that approximates this posterior distribution of the state trajectory with variable dimension. To illustrate the working of the proposed data assimilation framework, a case is studied in a gold mine system to estimate truck arrival times at the bottom of the vertical shaft. The results show that the framework is able to provide accurate estimation results in discrete event simulations; it is also shown that the framework is robust to errors both in the simulation model and in the data.