On Joint State Estimation and Model Learning using Gaussian Process Approximations

Kullberg, Anton

doi:10.3384/9789179291426

Search citation statements

Order By: Relevance

Paper Sections

Select...

State Estimation1

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Other1

Relationship

Self Cite0

Independent1

Authors

Journals

Cited by 1 publication

(1 citation statement)

References 28 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The material is by no means exhaustive but provides a solid foundation for understanding the publications relating to dynamical systems in this thesis. The material is based on [66,[92][93][94] and Paper D.…”

Section: State Estimationmentioning

confidence: 99%

Dynamic rEvolution : Adaptive state estimation via Gaussian processes and iterative filtering

Kullberg

2024

Linköping Studies in Science and Technology. Dissertations

View full text Add to dashboard Cite

Cover illustration: A Gaussian process model of the lateral acceleration experienced by a car driving around the Nürburgring, Germany. The acceleration profile is illustrated for a part of the track, specifically Kallenhard-Wehrseifen-Ex-Mühle. Title: A play on words relating to the theme of the thesis. Dynamic -the thesis studies dynamical systems; Evolution -dynamical systems evolve over time and the contributions are an evolution of the research field itself; rE(volution) -iterated algorithms are at the core of parts of the contributions.Linköping studies in science and technology. Dissertations.No. 2391

show abstract

Section: State Estimationmentioning

confidence: 99%

Dynamic rEvolution : Adaptive state estimation via Gaussian processes and iterative filtering

Kullberg

2024

Linköping Studies in Science and Technology. Dissertations

View full text Add to dashboard Cite

show abstract

On Joint State Estimation and Model Learning using Gaussian Process Approximations

Cited by 1 publication

References 28 publications

Dynamic rEvolution : Adaptive state estimation via Gaussian processes and iterative filtering

Dynamic rEvolution : Adaptive state estimation via Gaussian processes and iterative filtering

Contact Info

Product

Resources

About