Point-mass Filter with Functional Decomposition of Transient Density and Two-level Convolution

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F23%3A43969685" target="_blank" >RIV/49777513:23520/23:43969685 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S2405896323008765" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2405896323008765</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.ifacol.2023.10.509" target="_blank" >10.1016/j.ifacol.2023.10.509</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Point-mass Filter with Functional Decomposition of Transient Density and Two-level Convolution

Popis výsledku v původním jazyce

The paper deals with Bayesian state estimation using the point-mass filter with a particular focus on the prediction step involving the convolution of two grids of points. To reduce the computational costs of the step, a functional decomposition-based convolution was proposed by Tichavský et al. (2022), which approximates the transient probability density function over an approximation region. This paper addresses the problem of having spacious grids of points due to state uncertainty while the approximation region is kept small to preserve low computational complexity. A two-level convolution is proposed based on splitting the grids into subgrids and processing the convolution in the upper level for the subgrids and in the lower level for their points. An example demonstrates the proposed technique efficiency.

Název v anglickém jazyce

Point-mass Filter with Functional Decomposition of Transient Density and Two-level Convolution

Popis výsledku anglicky

The paper deals with Bayesian state estimation using the point-mass filter with a particular focus on the prediction step involving the convolution of two grids of points. To reduce the computational costs of the step, a functional decomposition-based convolution was proposed by Tichavský et al. (2022), which approximates the transient probability density function over an approximation region. This paper addresses the problem of having spacious grids of points due to state uncertainty while the approximation region is kept small to preserve low computational complexity. A two-level convolution is proposed based on splitting the grids into subgrids and processing the convolution in the upper level for the subgrids and in the lower level for their points. An example demonstrates the proposed technique efficiency.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

20205 - Automation and control systems

Projekt

<a href="/cs/project/GA22-11101S" target="_blank" >GA22-11101S: Tenzorový rozklad v aktivní diagnostice poruch pro stochastické rozlehlé systémy</a><br>

Návaznosti

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

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

Proceedings of the IFAC World Congress 2023

ISBN

978-1-71387-234-4

ISSN

2405-8963

e-ISSN

—

Počet stran výsledku

6

Strana od-do

6934-6939

Název nakladatele

Elsevier B.V.

Místo vydání

Japonsko

Místo konání akce

Yokohama, Japonsko

Datum konání akce

9. 7. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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