Reliable Convolution in Point-Mass Filter for a Class of Nonlinear Models
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F20%3A43959474" target="_blank" >RIV/49777513:23520/20:43959474 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.23919/FUSION45008.2020.9190218" target="_blank" >https://doi.org/10.23919/FUSION45008.2020.9190218</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.23919/FUSION45008.2020.9190218" target="_blank" >10.23919/FUSION45008.2020.9190218</a>
Alternative languages
Result language
angličtina
Original language name
Reliable Convolution in Point-Mass Filter for a Class of Nonlinear Models
Original language description
This paper is devoted to the Bayesian state estimation of the nonlinear stochastic dynamic systems. The stress is laid on the numerical solution to the Bayesian recursive relations by the point-mass filter for a class of state-space models with linear dynamics and nonlinear measurement. In particular, a novel reliable technique for convolution computation is proposed. The technique combines the standard point-mass-based convolution with a density-weighted integration to provide accurate results even for systems with small state noise. Several implementations of the technique are developed, theoretically analysed, and evaluated in a numerical study.
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/GC20-06054J" target="_blank" >GC20-06054J: Intelligent Distributed Estimation Architectures</a><br>
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Proceedings of the 2020 IEEE 23rd International Conference on Information Fusion (FUSION)
ISBN
978-0-578-64709-8
ISSN
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e-ISSN
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Number of pages
7
Pages from-to
1-7
Publisher name
IEEE
Place of publication
Sun City
Event location
Sun City, Jihoafrická republika
Event date
Jul 6, 2020
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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