Unscented Kalman Filter Applied to the Spacecraft Attitude Estimation with Euler Angles
The aim of this work is to test an algorithm to estimate, in real time, the attitude of an artificial satellite using real data supplied by attitude sensors that are on board of the CBERS-2 satellite (China Brazil Earth Resources Satellite). The real-time estimator used in this work for attitude det...
Main Authors: | , , |
---|---|
Format: | Article |
Language: | English |
Published: |
Hindawi Limited
2012-01-01
|
Series: | Mathematical Problems in Engineering |
Online Access: | http://dx.doi.org/10.1155/2012/985429 |
id |
doaj-6a8091786bc24a9c834cf73ce11f0c76 |
---|---|
record_format |
Article |
spelling |
doaj-6a8091786bc24a9c834cf73ce11f0c762020-11-24T22:49:02ZengHindawi LimitedMathematical Problems in Engineering1024-123X1563-51472012-01-01201210.1155/2012/985429985429Unscented Kalman Filter Applied to the Spacecraft Attitude Estimation with Euler AnglesRoberta Veloso Garcia0Helio Koiti Kuga1Maria Cecilia F. P. S. Zanardi2Space Mechanic and Control Division, INPE, 12227-010 São José dos Campos, SP, BrazilSpace Mechanic and Control Division, INPE, 12227-010 São José dos Campos, SP, BrazilDepartment of Mathematics, FEG, UNESP, 12516-410 Guaratinguetá, SP, BrazilThe aim of this work is to test an algorithm to estimate, in real time, the attitude of an artificial satellite using real data supplied by attitude sensors that are on board of the CBERS-2 satellite (China Brazil Earth Resources Satellite). The real-time estimator used in this work for attitude determination is the Unscented Kalman Filter. This filter is a new alternative to the extended Kalman filter usually applied to the estimation and control problems of attitude and orbit. This algorithm is capable of carrying out estimation of the states of nonlinear systems, without the necessity of linearization of the nonlinear functions present in the model. This estimation is possible due to a transformation that generates a set of vectors that, suffering a nonlinear transformation, preserves the same mean and covariance of the random variables before the transformation. The performance will be evaluated and analyzed through the comparison between the Unscented Kalman filter and the extended Kalman filter results, by using real onboard data.http://dx.doi.org/10.1155/2012/985429 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Roberta Veloso Garcia Helio Koiti Kuga Maria Cecilia F. P. S. Zanardi |
spellingShingle |
Roberta Veloso Garcia Helio Koiti Kuga Maria Cecilia F. P. S. Zanardi Unscented Kalman Filter Applied to the Spacecraft Attitude Estimation with Euler Angles Mathematical Problems in Engineering |
author_facet |
Roberta Veloso Garcia Helio Koiti Kuga Maria Cecilia F. P. S. Zanardi |
author_sort |
Roberta Veloso Garcia |
title |
Unscented Kalman Filter Applied to the Spacecraft Attitude Estimation with Euler Angles |
title_short |
Unscented Kalman Filter Applied to the Spacecraft Attitude Estimation with Euler Angles |
title_full |
Unscented Kalman Filter Applied to the Spacecraft Attitude Estimation with Euler Angles |
title_fullStr |
Unscented Kalman Filter Applied to the Spacecraft Attitude Estimation with Euler Angles |
title_full_unstemmed |
Unscented Kalman Filter Applied to the Spacecraft Attitude Estimation with Euler Angles |
title_sort |
unscented kalman filter applied to the spacecraft attitude estimation with euler angles |
publisher |
Hindawi Limited |
series |
Mathematical Problems in Engineering |
issn |
1024-123X 1563-5147 |
publishDate |
2012-01-01 |
description |
The aim of this work is to test an algorithm to estimate, in real time, the attitude of an artificial satellite using real data supplied by attitude sensors that are on board of the CBERS-2 satellite (China Brazil Earth Resources Satellite). The real-time estimator used in this work for attitude determination is the Unscented Kalman Filter. This filter is a new alternative to the extended Kalman filter usually applied to the estimation and control problems of attitude and orbit. This algorithm is capable of carrying out estimation of the states of nonlinear systems, without the necessity of linearization of the nonlinear functions present in the model. This estimation is possible due to a transformation that generates a set of vectors that, suffering a nonlinear transformation, preserves the same mean and covariance of the random variables before the transformation. The performance will be evaluated and analyzed through the comparison between the Unscented Kalman filter and the extended Kalman filter results, by using real onboard data. |
url |
http://dx.doi.org/10.1155/2012/985429 |
work_keys_str_mv |
AT robertavelosogarcia unscentedkalmanfilterappliedtothespacecraftattitudeestimationwitheulerangles AT heliokoitikuga unscentedkalmanfilterappliedtothespacecraftattitudeestimationwitheulerangles AT mariaceciliafpszanardi unscentedkalmanfilterappliedtothespacecraftattitudeestimationwitheulerangles |
_version_ |
1725677468065464320 |