Uncertainty Quantification in Deep Learning Based Kalman Filters

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Uncertainty Quantification in Deep Learning Based Kalman Filters

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Uncertainty Quantification in Deep Learning Based Kalman Filters

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

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

Počet stran výsledku

5

Strana od-do

13121-13125

Název nakladatele

IEEE

Místo vydání

Seoul

Místo konání akce

Seoul

Datum konání akce

14. 4. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001396233806072