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.
Grillo C., Montano F. (2019). Wind component estimation for UAS flying in turbulent air. AEROSPACE SCIENCE AND TECHNOLOGY, 93 [10.1016/j.ast.2019.105317].
Wind component estimation for UAS flying in turbulent air
Grillo C.
;Montano F.
2019-01-01
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.
| File | Dimensione | Formato | |
|---|---|---|---|
|
Wind component estimation for UAS flying in turbulent air_per iris.pdf
Solo gestori archvio
Tipologia:
Pre-print
Dimensione
596.32 kB
Formato
Adobe PDF
|
596.32 kB | Adobe PDF | Visualizza/Apri Richiedi una copia |
|
1-s2.0-S1270963818326762-main.pdf
Solo gestori archvio
Descrizione: Articolo completo
Tipologia:
Versione Editoriale
Dimensione
1.1 MB
Formato
Adobe PDF
|
1.1 MB | Adobe PDF | Visualizza/Apri Richiedi una copia |
I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
