DANSE: Data-driven Non-linear State Estimation of Model-free Process in Unsupervised Learning Setup

arXiv:2306.03897v2 Announce Type: replace-cross
Abstract: We address the tasks of Bayesian state estimation and forecasting for a model-free process in an unsupervised learning setup. For a model-free process, we do not have any a-priori knowledge of the process dynamics. In the article, we propose DANSE -- a Data-driven Nonlinear State Estimation method. DANSE provides a closed-form posterior of the state of the model-free process, given linear measurements of the state. In addition, it provides a closed-form posterior for forecasting. A …

abstract article arxiv bayesian cs.lg cs.sy data data-driven dynamics eess.sp eess.sy forecasting free knowledge linear non-linear process setup state tasks type unsupervised unsupervised learning