28 citations · 32 across the 10 of their papers we have counts for
10 papers
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…
Exploiting Hankel-Toeplitz Structures for Fast Computation of Kernel Precision Matrices
Frida Viset, Anton Kullberg, Frederiek Wesel +1
The Hilbert-space Gaussian Process (HGP) approach offers a hyperparameter-independent basis function approximation for speeding up Gaussian Process (GP) inference by projecting the…
Dynamically Iterated Filters: A unified framework for improved iterated filtering and smoothing
Anton Kullberg, Martin A. Skoglund, Isaac Skog +1
Typical iterated filters, such as the iterated extended Kalman filter (IEKF), iterated unscented Kalman filter (IUKF), and iterated posterior linearization filter (IPLF), have been…
Extended target tracking utilizing machine-learning software -- with applications to animal classification
Magnus Malmström, Anton Kullberg, Isaac Skog +2
This paper considers the problem of detecting and tracking objects in a sequence of images. The problem is formulated in a filtering framework, using the output of object-detection…
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…