Point-mass filter with functional decomposition of transient density and two-level convolution
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Point-mass filter with functional decomposition of transient density and two-level convolution
Original language description
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.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
20205 - Automation and control systems
Result continuities
Project
<a href="/en/project/GA22-11101S" target="_blank" >GA22-11101S: Tensor Decomposition in Active Fault Diagnosis for Stochastic Large Scale Systems</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
IFAC-PapersOnLine. Volume 56, Issue 2 - 22nd IFAC World Congress
ISBN
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ISSN
2405-8963
e-ISSN
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Number of pages
6
Pages from-to
7516-7520
Publisher name
Elsevier
Place of publication
Amsterdam
Event location
Yokohama
Event date
Jul 9, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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