Adaptive Kalman Filtering Based on Subjective Logic Self-Assessment
Loading...
Date
2024-10-11
Authors
Griebel, Thomas
Müller, Johannes
Buchholz, Michael
Dietmayer, Klaus
Journal Title
Journal ISSN
Volume Title
Publication Type
Beitrag zu einer Konferenz
Published in
2024 27th International Conference on Information Fusion (FUSION)
Abstract
Monitoring and self-assessment of tracking algorithms are essential in modern automated driving systems. However, the further use of this self-assessment information is another growing and not thoroughly studied area of research. One option is to adapt the parameters configured in the tracking algorithm online to obtain better and more robust tracking results directly. The paper proposes a novel overall concept and framework for adaptive Kalman filtering using subjective logic. Based on a self-assessment method, we present multiple variants of adaptive strategies to adapt the noise assumptions online for Kalman filtering. This paper focuses mainly on adaptation procedures for multi-sensor Kalman filters. The proposed method is evaluated in various experiments and compared with state-of-the-art adaptive Kalman filters.
Description
Faculties
Fakultät für Ingenieurwissenschaften, Informatik und Psychologie
Institutions
Institut für Mess-, Regel- und Mikrotechnik
Citation
DFG Project uulm
EU Project THU
EVENTS / ReliablE in-Vehicle pErception and decisioN-making in complex environmenTal conditionS / EC / HE / 101069614
Other projects THU
License
CC BY 4.0 International
Is version of
Has version
Supplement to
Supplemented by
Has erratum
Erratum to
Has Part
Part of
DOI external
DOI external
10.23919/FUSION59988.2024.10706328
Institutions
Periodical
Degree Program
DFG Project THU
item.page.thu.projectEU
item.page.thu.projectOther
Series
Keywords
Adaptive filtering, Self-assessment, Subjective logic, Kalman-Filter, Adaptive filters, DDC 620 / Engineering & allied operations