Importance Gauss-Hermite Gaussian Filter for Models with Non-Additive Non-Gaussian Noises
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F49777513%3A23520%2F21%3A43962474" target="_blank" >RIV/49777513:23520/21:43962474 - isvavai.cz</a>
Result on the web
<a href="https://ieeexplore.ieee.org/document/9626832" target="_blank" >https://ieeexplore.ieee.org/document/9626832</a>
DOI - Digital Object Identifier
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Alternative languages
Result language
angličtina
Original language name
Importance Gauss-Hermite Gaussian Filter for Models with Non-Additive Non-Gaussian Noises
Original language description
The paper deals with the state estimation of nonlinear stochastic systems with non-additive non-Gaussian noises. A new algorithm is proposed based on the computationally efficient Gaussian filter. The non-additivity and non-Gaussianity of the noises prevents the usage of standard quadratures to evaluate the moment integrals present in the Gaussian filter as these are not Gauss-weighted. The proposed algorithm leverages the importance Gauss-Hermite method to evaluate the integrals by means of the Gaussian proposal PDF. In order to improve the evaluation quality, an iterative improvement of the proposal PDF is employed. The paper also discusses the algorithm for special cases of the model with either process or measurement noise being additive yet non-Gaussian. The performance of the proposed algorithm is illustrated using a numerical example.
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
2021
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 2021 IEEE 24th International Conference on Information Fusion (FUSION)
ISBN
978-1-73774-971-4
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
Nov 1, 2021
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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