| Title: | Accurate Density-Weighted Convolution for Point-Mass Filter and Predictor |
| Authors: | Duník, Jindřich Straka, Ondřej Matoušek, Jakub Brandner, Marek |
| Citation: | DUNÍK, J. STRAKA, O. MATOUŠEK, J. BRANDNER, M. Accurate Density-Weighted Convolution for Point-Mass Filter and Predictor. IEEE Transactions on Aerospace and Electronic Systems, 2021, roč. 57, č. 6, s. 3574-3584. ISSN: 0018-9251 |
| Issue Date: | 2021 |
| Publisher: | IEEE |
| Document type: | článek article |
| URI: | 2-s2.0-85105868526 http://hdl.handle.net/11025/47006 |
| ISSN: | 0018-9251 |
| Keywords in different language: | state estimation;Bayesian inference;nonlinear systems;point-mass filter |
| Abstract in different language: | This paper deals with the Bayesian state estimation of nonlinear stochastic dynamic systems. The stress is laid on the numerical solution to the Chapman-Kolmogorov equation, which governs the prediction step of the point-mass filter and predictor, using the convolution. A novel density-weighted convolution is proposed, which provides an accurate predictive probability density function even for models with small state noise, where the standard solution fails. Two implementations of the solution are proposed, theoretically analyzed, and evaluated in a numerical study. |
| Rights: | Plný text je přístupný v rámci univerzity přihlášeným uživatelům. © IEEE |
| Appears in Collections: | Články / Articles (KMA) Články / Articles (KKY) OBD |
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|---|---|---|---|
| article_IEEETAES_DuStMaBr2021.pdf | 1,94 MB | Adobe PDF | View/Open Request a copy |
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