2022
Uncertainty in Data-Driven Kalman Filtering for Partially Known State-Space Models
ICASSP 2022accepted
Providing a metric of uncertainty alongside a state estimate is often crucial when tracking a dynamical system. Classic state estimators, such as the Kalman filter (KF), provide a time-dependent uncertainty measure from knowledge of the underlying statistics; however, deep learning based tracking sy…