Bayesian Knowledge Transfer for a Kalman Fixed-Lag Interval Smoother

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26620%2F26%3A0199372" target="_blank" >RIV/00216305:26620/26:0199372 - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/11036741" target="_blank" >https://ieeexplore.ieee.org/document/11036741</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/LCSYS.2025.3580047" target="_blank" >10.1109/LCSYS.2025.3580047</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Bayesian Knowledge Transfer for a Kalman Fixed-Lag Interval Smoother

Popis výsledku v původním jazyce

A Bayesian knowledge transfer mechanism that leverages external information to improve the performance of the Kalman fixed-lag interval smoother (FLIS) is proposed. Exact knowledge of the external observation model is assumed to be missing, which hinders the direct application of Bayes' rule in traditional transfer learning approaches. This limitation is overcome by the fully probabilistic design, conditioning the targeted task of state estimation on external information. To mitigate the negative impact of inaccurate external data while leveraging precise information, a latent variable is introduced. Favorably, in contrast to a filter, FLIS retrospectively refines past decisions up to a fixed time horizon, reducing the accumulation of estimation error and consequently improving the performance of state inference. Simulations indicate that the proposed algorithm better exploits precise external knowledge compared to a similar technique and achieves comparable results when the information is imprecise.

Název v anglickém jazyce

Bayesian Knowledge Transfer for a Kalman Fixed-Lag Interval Smoother

Popis výsledku anglicky

A Bayesian knowledge transfer mechanism that leverages external information to improve the performance of the Kalman fixed-lag interval smoother (FLIS) is proposed. Exact knowledge of the external observation model is assumed to be missing, which hinders the direct application of Bayes' rule in traditional transfer learning approaches. This limitation is overcome by the fully probabilistic design, conditioning the targeted task of state estimation on external information. To mitigate the negative impact of inaccurate external data while leveraging precise information, a latent variable is introduced. Favorably, in contrast to a filter, FLIS retrospectively refines past decisions up to a fixed time horizon, reducing the accumulation of estimation error and consequently improving the performance of state inference. Simulations indicate that the proposed algorithm better exploits precise external knowledge compared to a similar technique and achieves comparable results when the information is imprecise.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

20205 - Automation and control systems

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

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

Rok uplatnění

2025

Kód důvěrnosti údajů

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

Název periodika

IEEE Control Systems Letters

ISSN

—

e-ISSN

2475-1456

Svazek periodika

9

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

6

Strana od-do

2037-2042

Kód UT WoS článku

001550786500005

EID výsledku v databázi Scopus

2-s2.0-105008813223