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%2F67985556%3A_____%2F23%3A00574015" target="_blank" >RIV/67985556:_____/23:00574015 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.ifacol.2023.10.509" target="_blank" >http://dx.doi.org/10.1016/j.ifacol.2023.10.509</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 (PDF) 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 sub-grids and processing the convolution in the upper level for the sub-grids and in the lower level for their points. A numerical example demonstrates the efficiency of the proposed technique.

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 (PDF) 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 sub-grids and processing the convolution in the upper level for the sub-grids and in the lower level for their points. A numerical example demonstrates the efficiency of the proposed technique.

Druh

D - Stať ve sborníku

CEP obor

—

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

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2023

Kód důvěrnosti údajů

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

Název statě ve sborníku

IFAC-PapersOnLine. Volume 56, Issue 2 - 22nd IFAC World Congress

ISBN

—

ISSN

2405-8963

e-ISSN

—

Počet stran výsledku

6

Strana od-do

7516-7520

Název nakladatele

Elsevier

Místo vydání

Amsterdam

Místo konání akce

Yokohama

Datum konání akce

9. 7. 2023

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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