| Title: | Cooperative Unscented Kalman Filter with Bank of Scaling Parameter Values |
| Authors: | Duník, Jindřich Straka, Ondřej Hanebeck, Uwe D. |
| 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 |
| Issue Date: | 2021 |
| Publisher: | IEEE |
| Document type: | konferenční příspěvek ConferenceObject |
| URI: | 2-s2.0-85123396400 http://hdl.handle.net/11025/47136 |
| ISBN: | 978-1-73774-971-4 |
| Keywords in different language: | Nonlinear filtering, Gaussian estimators, Bayesian relations |
| 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. |
| Abstract in different language: | 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. |
| Rights: | Plný text je přístupný v rámci univerzity přihlášeným uživatelům. © ISIF |
| Appears in Collections: | Konferenční příspěvky / Conference Papers (KKY) OBD |
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