Special Session 113: Recent Advances in Uncertainty Quantification and Scientific Machine Learning with Applications to Complex Dynamical Systems

New results in feedback particle filter
Sumith Reddy Anugu
TU Ilmenau
Germany
Co-Author(s):    Sumith Reddy Anugu (TU Ilmenau), Jana de Wiljes (TU Ilmenau), Gottfried Hastermann (TU Ilmenau)
Abstract:
Feedback particle filter is known to provide a way to compute the filter (conditional) distribution of the associated filtering model. In this talk, we present certain new results in the context of feedback particle filter. We first present the well-posedness of the associated Poisson equation (which is crucial to the implementation of the feedback particle filter) under certain easily verifiable sufficient conditions. Then, we introduce a new approximation procedure for the feedback particle filter (that is computationally more efficient), which makes use of a `local` version of the associated Poisson equation.