A Bayesian approach for parameter estimation in railway systems

Abstract

In this paper, we address the problem of parameter estimation in railway systems. For this purpose, a physical model of the train based on the fundamental principle of dynamics is proposed. Then, the parameter estimation is handled via an approach using a combination of Expectation-Maximization algorithm and Sequential Monte Carlo methods. The experiments performed both on synthetic and real data show the efficiency of the considered method.