Efficient Gaussian Mixture Filters Based on Transition Density Approximation
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F25%3A43976521" target="_blank" >RIV/49777513:23520/25:43976521 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.23919/FUSION65864.2025.11124060" target="_blank" >https://doi.org/10.23919/FUSION65864.2025.11124060</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.23919/FUSION65864.2025.11124060" target="_blank" >10.23919/FUSION65864.2025.11124060</a>
Alternative languages
Result language
angličtina
Original language name
Efficient Gaussian Mixture Filters Based on Transition Density Approximation
Original language description
Gaussian mixture filters for nonlinear systems usually rely on severe approximations when calculating mixtures in the prediction and filtering step. Thus, offline approximations of noise densities by Gaussian mixture densities to reduce the approximation error have been proposed. This results in exponential growth in the number of components, requiring ongoing component reduction, which is computationally complex. In this paper, the key idea is to approximate the true transition density by an axis-aligned Gaussian mixture, where two different approaches are derived. These approximations automatically ensure a constant number of components in the posterior densities without the need for explicit reduction. In addition, they allow a trade-off between estimation quality and computational complexity.
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/EH22_008%2F0004590" target="_blank" >EH22_008/0004590: Robotics and advanced industrial production</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace
Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
2025 28th International Conference on Information Fusion (FUSION)
ISBN
978-1-03-705623-9
ISSN
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e-ISSN
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Number of pages
8
Pages from-to
1-8
Publisher name
IEEE
Place of publication
Rio de Janiero, Brazílie
Event location
Rio de Janiero, Brazílie
Event date
Jul 7, 2025
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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