The time update is a critical component in navigation and tracking filters. Usually, physical dynamical models based on Newton’s laws are used to predict the motion of the vehicle. In this project, we aim to use data-driven methods and machine learning to learn the dynamics of the vehicle.

In many cases, the motion patterns of objects are very repeatable, but beforehand not known. This might be boats drifting along streams or cars following traffic rules. Today, the standard methods simply assume no more knowledge of the motion than that physical limitations with respect to accelerations etc are fulfilled.

Learning to time updateObviously, better knowledge of the underlying motion patterns would be of huge benefit. Learning these motion patterns is a problem that combines theory from classic estimation theory, as well as modern data driven machine learning methods.

The main challenge lies in exploiting the synergies possible by combining the two approaches in one common framework.


Researchers

Publications

2023

Filipe Marques Barbosa, Anton Kullberg, Johan Löfberg (2023) Fast or Cheap: Time and Energy Optimal Control of Ship-to-Shore Cranes IFAC-PapersOnLine, Vol. 56, p. 3126-3131 Continue to DOI
Anton Kullberg, Isaac Skog, Gustaf Hendeby (2023) Iterated Filters for Nonlinear Transition Models 2023 26th International Conference on Information Fusion (FUSION 2023), Charleston, S.C., U.S., June 27-30, 2023., p. 1-8 Continue to DOI

2021

Anton Kullberg, Isaac Skog, Gustaf Hendeby (2021) Learning Motion Patterns in AIS Data and Detecting Anomalous Vessel Behavior 2021 IEEE 24th International Conference on Information Fusion (FUSION), p. 612-619 Continue to DOI
Anton Kullberg (2021) On Joint State Estimation and Model Learning using Gaussian Process Approximations
Anton Kullberg, Isaac Skog, Gustaf Hendeby (2021) Online Joint State Inference and Learning of Partially Unknown State-Space Models IEEE Transactions on Signal Processing, Vol. 69, p. 4149-4161 Continue to DOI

2020

Anton Kullberg, Isaac Skog, Gustaf Hendeby (2020) Learning Driver Behaviors Using A Gaussian Process Augmented State-Space Model Proceedings of 2020 23rd International Conference on Information Fusion (FUSION 2020), p. 530-536 Continue to DOI

WASP Sensor fusion research