28 citations · 32 across the 10 of their papers we have counts for
5 papers · 1 filter
Adaptive Basis Function Selection for Computationally Efficient Predictions
Anton Kullberg, Frida Viset, Isaac Skog +1
Basis Function (BF) expansions are a cornerstone of any engineer's toolbox for computational function approximation which shares connections with both neural networks and Gaussian…
Unified Linearization-based Nonlinear Filtering
Anton Kullberg, Isaac Skog, Gustaf Hendeby
This letter shows that the following three classes of recursive state estimation filters: standard filters, such as the extended Kalman filter; iterated filters, such as the iterat…
On the Relationship Between Iterated Statistical Linearization and Quasi-Newton Methods
Anton Kullberg, Martin A. Skoglund, Isaac Skog +1
This letter investigates relationships between iterated filtering algorithms based on statistical linearization, such as the iterated unscented Kalman filter (IUKF), and filtering…
Iterated Filters for Nonlinear Transition Models
Anton Kullberg, Isaac Skog, Gustaf Hendeby
A new class of iterated linearization-based nonlinear filters, dubbed dynamically iterated filters, is presented. Contrary to regular iterated filters such as the iterated extended…
Learning Driver Behaviors Using A Gaussian Process Augmented State-Space Model
Anton Kullberg, Isaac Skog, Gustaf Hendeby
An inference method for Gaussian process augmented state-space models are presented. This class of grey-box models enables domain knowledge to be incorporated in the inference proc…