Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Duník, Jindřich | |
| dc.contributor.author | Straka, Ondřej | |
| dc.contributor.author | Hanebeck, Uwe D. | |
| dc.date.accessioned | 2022-03-14T11:00:23Z | - |
| dc.date.available | 2022-03-14T11:00:23Z | - |
| dc.date.issued | 2021 | |
| dc.identifier.citation | DUNÍK, J. STRAKA, O. HANEBECK, UD. Cooperative Unscented Kalman Filter with Bank of Scaling Parameter Values. In Proceedings of the 2021 IEEE 24th International Conference on Information Fusion (FUSION). Sun City: IEEE, 2021. s. 1-8. ISBN: 978-1-73774-971-4 , ISSN: neuvedeno | cs |
| dc.identifier.isbn | 978-1-73774-971-4 | |
| dc.identifier.uri | 2-s2.0-85123396400 | |
| dc.identifier.uri | http://hdl.handle.net/11025/47136 | |
| dc.description.abstract | Článek je věnován odhadu stavu nelineárních dynamických stochastických systémů. Důraz je kladen na unscentovaný Kalmanův filtr a volbu jeho škálovacího parametru. Nová technika návrhu parametru, která je založena na multi-modelovém přístupu, je navržena a ověřena v numerických simulacích. | cs |
| dc.format | 8 s. | cs |
| dc.format.mimetype | application/pdf | |
| dc.language.iso | en | en |
| dc.publisher | IEEE | en |
| dc.relation.ispartofseries | Proceedings of the 2021 IEEE 24th International Conference on Information Fusion (FUSION) | en |
| dc.rights | Plný text je přístupný v rámci univerzity přihlášeným uživatelům. | cs |
| dc.rights | © ISIF | en |
| dc.title | Cooperative Unscented Kalman Filter with Bank of Scaling Parameter Values | en |
| dc.type | konferenční příspěvek | cs |
| dc.type | ConferenceObject | en |
| dc.rights.access | restrictedAccess | en |
| dc.type.version | publishedVersion | en |
| dc.description.abstract-translated | This paper is devoted to the Bayesian state estimation of the nonlinear stochastic dynamic systems. The stress is laid on Gaussian unscented Kalman filter (UKF) and, in particular, on a setting of its scaling parameter, which significantly affects the UKF estimation performance. Compared to the standard UKF design, where one scaling parameter per a time instant is selected, the proposed cooperative UKF combines estimates of the set of UKFs each designed with different value of the scaling parameter. The cooperative UKF reformulates the UKF scaling parameter selection task as the multiple model approach, which allows to extract more information from the measurement to provide estimates of better quality as indicated by the numerical simulations. | en |
| dc.subject.translated | Nonlinear filtering, Gaussian estimators, Bayesian relations | en |
| dc.type.status | Peer-reviewed | en |
| dc.identifier.obd | 43933472 | |
| dc.project.ID | GC20-06054J/Inteligentní distribuované architektury pro odhad stavu | cs |
| dc.project.ID | SGS-2019-020/Rozvoj a využití kybernetických systémů identifikace, diagnostiky a řízení 4 | cs |
| Appears in Collections: | Konferenční příspěvky / Conference Papers (KKY) OBD | |
Files in This Item:
| File | Size | Format | |
|---|---|---|---|
| article_FUSION2021_DuStHa.pdf | 906,67 kB | Adobe PDF | View/Open Request a copy |
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