Normalising Flow-based Differentiable Particle Filters

arXiv:2403.01499v1 Announce Type: new
Abstract: Recently, there has been a surge of interest in incorporating neural networks into particle filters, e.g. differentiable particle filters, to perform joint sequential state estimation and model learning for non-linear non-Gaussian state-space models in complex environments. Existing differentiable particle filters are mostly constructed with vanilla neural networks that do not allow density estimation. As a result, they are either restricted to a bootstrap particle filtering framework or employ predefined distribution families (e.g. Gaussian distributions), limiting …

abstract arxiv cs.lg differentiable eess.sp environments filters flow linear networks neural networks non-linear particle space state type