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...

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Main Authors: Roberta Veloso Garcia, Helio Koiti Kuga, Maria Cecilia F. P. S. Zanardi
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
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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
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