Robust Kalman Filtering With Probabilistic Uncertainty in System Parameters

In this letter, we propose a robust Kalman filtering framework for systems with probabilistic uncertainty in system parameters. We consider two cases, namely discrete time systems, and continuous measurements. The uncertainty, characterized by mean variance of the states, is propagated using conditional expectations polynomial chaos expansion framework. results obtained proposed filter are compared existing filters literature. demonstrates better performance terms estimation error rate convergence.