State Estimation With Model Uncertainty Using Structure Variational Bayesian and Transfer Learning

This paper proposes a novel approach to address parameter uncertainties for state estimation in Markovian jump linear systems by leveraging transfer learning. Assume that the source domain model is available and reliable, and the target domain model has significant model parameter uncertainties. To enhance estimation performance in the target domain, the proposed method transfers model knowledge from the source domain and adjusts it using a tuning factor before incorporating it into the target domain estimator. More specifically, this approach involves transferring the modified probability density functions of state prediction from the source domain to the target domain and determining the tuning factor via structure variational Bayesian inference using measurements in the target domain. Using numerical examples and a 1-DOF torsion system, we showcase the competitiveness of the proposed state estimator compared to the existing robust state estimation methods when dealing with parameter uncertainties. The results highlight its capability to improve estimation accuracy in practical scenarios, showcasing its potential for real-world applications.