Kernel Variational Inference Flow for Nonlinear Filtering Problem

Abstract

We present a novel particle flow for sampling called kernel variational inference flow (KVIF). KVIF do not require the explicit formula of the target distribution which is usually unknown in filtering problem. Therefore, it can be applied to construct filters with higher accuracy in the update stage. Such an improvement has theoretical assurance. Some numerical experiments for comparison with other classical filters are also demonstrated.