Tram Localization using Soft-Constrained Iterated Kalman Filter with Optimal Step Size Control

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F24%3A00377489" target="_blank" >RIV/68407700:21230/24:00377489 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1109/MFI62651.2024.10705771" target="_blank" >http://dx.doi.org/10.1109/MFI62651.2024.10705771</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/MFI62651.2024.10705771" target="_blank" >10.1109/MFI62651.2024.10705771</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Tram Localization using Soft-Constrained Iterated Kalman Filter with Optimal Step Size Control

Popis výsledku v původním jazyce

The paper describes a way to incorporate soft constraints to iterated Kalman filter and its application to tram localization. The localization task is motivated by a suboptimal performance of a currently used hard-constrained localization solution and is then tackled by introducing a framework allowing to insert various constraints into the Kalman filter scheme. The filtering (data update) step of the iterated Kalman filter is interpreted as an instance of a non-linear least squares problem for which the step size in each iteration can be chosen in the optimal manner, improving the overall state estimate convergence. The described estimation framework is showcased on real-life data and compared against a hardconstrained Kalman filter scheme which resembles the one originally motivating the task. While the presented solution does not provide a substantial quantifiable improvement in quality of estimate in this particular case, it is much more versatile and does not require prior specific choice of tramway tracks. Finally, the introduced scheme is employed on the motivating example with a discussion of possible further enhancements.

Název v anglickém jazyce

Tram Localization using Soft-Constrained Iterated Kalman Filter with Optimal Step Size Control

Popis výsledku anglicky

The paper describes a way to incorporate soft constraints to iterated Kalman filter and its application to tram localization. The localization task is motivated by a suboptimal performance of a currently used hard-constrained localization solution and is then tackled by introducing a framework allowing to insert various constraints into the Kalman filter scheme. The filtering (data update) step of the iterated Kalman filter is interpreted as an instance of a non-linear least squares problem for which the step size in each iteration can be chosen in the optimal manner, improving the overall state estimate convergence. The described estimation framework is showcased on real-life data and compared against a hardconstrained Kalman filter scheme which resembles the one originally motivating the task. While the presented solution does not provide a substantial quantifiable improvement in quality of estimate in this particular case, it is much more versatile and does not require prior specific choice of tramway tracks. Finally, the introduced scheme is employed on the motivating example with a discussion of possible further enhancements.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

<a href="/cs/project/CK03000269" target="_blank" >CK03000269: Pokročilé metody zpracování palubních dat v systémech V2X</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název statě ve sborníku

2024 IEEE International Conference on Multisensor Fusion and Integration for Intelligent Systems (MFI)

ISBN

979-8-3503-6803-1

ISSN

2835-947X

e-ISSN

2767-9357

Počet stran výsledku

6

Strana od-do

—

Název nakladatele

IEEE

Místo vydání

Piscataway

Místo konání akce

Plzeň

Datum konání akce

4. 9. 2024

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

—