Efficient State Estimation of Discrete-Timed Automata

State estimation is a fundamental method in control theory that has applications in privacy, fault diagnosis, and the verification of other state inference properties. State estimation methods for timed automata rely on discretizing time into atomic steps. These discrete time steps are enumerated in separate states, significantly limiting scalability for large, realistic systems. In this paper, we propose a more efficient state estimation method for discrete-timed automata. The key idea of our approach is to avoid the enumeration of single time steps when possible. We provide a formal definition of a new state estimator model and an efficient algorithmic approach to derive it from discrete-timed automata. We validate our method on 11 realistic case studies and show a significant decrease in computational costs.