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EnglishOne of the most important problem of autonomous flight for UAS is the wind identification, especially for small scale vehicles. This research focusses on an identification methodology based on the Extended Kalman Filter (EKF). In particular authors focus their attention on.the filter tuning problem. The proposed procedure requires low computational power, so it is very useful for UAS. Besides it allows a robust wind component identification even when, as it is usually, the measurement data set is affected by noticeable noises. (C) 2019 Elsevier Masson SAS. All rights reserved.
Grillo, C., & Montano, F. (2019). Wind component estimation for UAS flying in turbulent air. AEROSPACE SCIENCE AND TECHNOLOGY, 93, 105317.
| Data di pubblicazione: | 2019 |
| Titolo: | Wind component estimation for UAS flying in turbulent air |
| Autori: | |
| Citazione: | Grillo, C., & Montano, F. (2019). Wind component estimation for UAS flying in turbulent air. AEROSPACE SCIENCE AND TECHNOLOGY, 93, 105317. |
| Rivista: | |
| Digital Object Identifier (DOI): | http://dx.doi.org/10.1016/j.ast.2019.105317 |
| Abstract: | One of the most important problem of autonomous flight for UAS is the wind identification, especially for small scale vehicles. This research focusses on an identification methodology based on the Extended Kalman Filter (EKF). In particular authors focus their attention on.the filter tuning problem. The proposed procedure requires low computational power, so it is very useful for UAS. Besides it allows a robust wind component identification even when, as it is usually, the measurement data set is affected by noticeable noises. (C) 2019 Elsevier Masson SAS. All rights reserved. |
| URL: | https://www.journals.elsevier.com/aerospace-science-and-technology |
| Settore Scientifico Disciplinare: | Settore ING-IND/03 - Meccanica Del Volo |
| Appare nelle tipologie: | 1.01 Articolo in rivista |
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http://hdl.handle.net/10447/386911
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