Uncertainty Quantification in Deep Learning Based Kalman Filters
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F24%3A43973099" target="_blank" >RIV/49777513:23520/24:43973099 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1109/ICASSP48485.2024.10447987" target="_blank" >https://doi.org/10.1109/ICASSP48485.2024.10447987</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/ICASSP48485.2024.10447987" target="_blank" >10.1109/ICASSP48485.2024.10447987</a>
Alternative languages
Result language
angličtina
Original language name
Uncertainty Quantification in Deep Learning Based Kalman Filters
Original language description
Various algorithms combine deep neural networks (DNNs) and Kalman filters (KFs) to learn from data to track in complex dynamics. Unlike classic KFs, DNN-based systems do not naturally provide the error covariance alongside their estimate, which is of great importance in some applications, e.g., navigation. To bridge this gap, in this work we study error covariance extraction in DNN-aided KFs. We examine three main approaches that are distinguished by the ability to associate internal features with meaningful KF quantities such as the Kalman gain (KG) and prior covariance. We identify the differences between these approaches in their requirements and their effect on the training of the system. Our numerical study demonstrates that the above approaches allow DNN-aided KFs to extract error covariance, with most accurate error prediction provided by model-based/data-driven designs.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
20205 - Automation and control systems
Result continuities
Project
—
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
ISBN
979-8-3503-4485-1
ISSN
1520-6149
e-ISSN
2379-190X
Number of pages
5
Pages from-to
13121-13125
Publisher name
IEEE
Place of publication
Seoul
Event location
Seoul
Event date
Apr 14, 2024
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
001396233806072