@InProceedings{GarciaKugaZana:2013:EfFiKa,
author = "Garcia, Roberta Veloso and Kuga, Helio Koiti and Zanardi, Maria
Cec{\'{\i}}lia Fran{\c{c}}a de Paula Santos",
affiliation = "{} and {Instituto Nacional de Pesquisas Espaciais (INPE)}",
title = "Efici{\^e}ncia do Filtro de Kalman Unscented na
estima{\c{c}}{\~a}o de atitude utilizando dados reais do
sat{\'e}lite CBERS",
booktitle = "Anais...",
year = "2013",
editor = "Epiphanio, Jos{\'e} Carlos Neves and Galv{\~a}o, L{\^e}nio
Soares",
pages = "2241--2249",
organization = "Simp{\'o}sio Brasileiro de Sensoriamento Remoto, 16. (SBSR)",
publisher = "Instituto Nacional de Pesquisas Espaciais (INPE)",
address = "S{\~a}o Jos{\'e} dos Campos",
abstract = "The aim of this work is to compare the performance of the
unscented Kalman filter (UKF) with the extended Kalman filter
(EKF) in the attitude estimation nonlinear problems when the
filters are subject to inaccurate initial conditions. For
nonlinear systems the unscented Kalman filter uses a carefully
selected set of sample points to more accurately map the
probability distribution than the linearization of the standard
extended Kalman filter, leading to faster convergence from
inaccurate initial conditions in attitude estimation problems. In
this study, the attitude of a satellite is estimated, simulating
real time conditions using real data supplied by gyroscopes,
infrared Earth sensors and digital Sun sensors. These sensors are
on board the CBERS-2 satellite and the measurements were collected
by the Satellite Control Centre of INPE. The satellite attitude is
described by Euler angles, due to its easy geometric
interpretation and the filter formulation is based on standard
attitude-vector measurements using a gyro-based model for attitude
propagation. Then by the degraded initial conditions it is
possible to conclude that UKF is more efficient and accuracy than
EKF. In relation with the process time, the UKF is competitive
because although it demands a more time for the estimation
process, the CPU time isnīt proportional to the generated
sigma-points number. In the same way UKF can be applied in real
time problem.",
conference-location = "Foz do Igua{\c{c}}u",
conference-year = "13-18 abr. 2013",
isbn = "{978-85-17-00066-9 (Internet)} and {978-85-17-00065-2 (DVD)}",
label = "1456",
language = "pt",
organisation = "Instituto Nacional de Pesquisas Espaciais (INPE)",
ibi = "3ERPFQRTRW34M/3E7GLET",
url = "http://urlib.net/ibi/3ERPFQRTRW34M/3E7GLET",
targetfile = "p1456.pdf",
type = "CBERS: Avalia{\c{c}}{\~a}o e Aplica{\c{c}}{\~o}es",
urlaccessdate = "18 abr. 2024"
}