https://doi.org/10.1140/epjs/s11734-023-01061-7
Regular Article
Attitude estimation for remote sensing satellite CBERS-4 using unscented Gaussian sum filter
1
Gama Campus (FGA), University of Brasilia (UnB), Área Especial de Indústria, Projeção A, Setor Leste (Gama), 72444-240, Brasília, Federal District, Brazil
2
Lorena School of Engineering (EEL), University of São Paulo (USP), Estrada Municipal do Campinho, S/N. Ponte Nova, 12602-810, Lorena, São Paulo, Brazil
3
Collaborative Laboratory (CoLAB), Center of Engineering and Product Development (CEiiA), PACT, Rua Luís Adelino Fonseca, 1, 7005-841, Évora, Évora, Portugal
4
Space Mechanics and Control Division (CMC), National Institute for Space Research (INPE), Av dos Astronautas, 1758, Jd. da Granja, 12227-010, São José dos Campos, São Paulo, Brazil
5
Mathematics Department (DM), São Paulo University (UNESP), Av. Ariberto Pereira da Cunha, 333, Pedregulho, 12516-410, Guaratinguetá, São Paulo, Brazil
6
Engineering, Modeling and Applied Social Sciences Center (CECS), Federal University of ABC (UFABC), Av. dos Estados, 5001, Bangú, 09210-580, Santo André, São Paulo, Brazil
Received:
25
April
2023
Accepted:
24
November
2023
Published online:
13
December
2023
The fundamental concept in a Gaussian Sum Filter (GSF) is to use a finite set of Gaussian distributions to estimate and to construct the probability density function (pdf) using Bayesian estimation approach. The goal for the GSF is approximates the predicted and posterior probabilities densities functions (pdfs) as a finite number of weighted sums of Gaussian densities distributions as has been proposed in the literature. The central idea in the Unscented Gaussian sum Filter (UGSF) is to represent non-Gaussian densities using sigma points by an Unscented Transformation. For nonlinear systems, such as attitude dynamics and attitude kinematics, the posterior pdf may not be Gaussian though, which may lead to problems in the Extend Kalman Filter (EKF) and Unscented Kalman Filter (UKF). The purpose of this research is to apply the UGSF and GSF for CBERS-4 attitude and gyros bias estimation, remote sensing satellite recently in operation. The results for attitude estimation the UGSF has a processing time 5.6 times greater than the UKF; 6.6 times less than the GSF and 19.9 times less than the Particle Filter (PF). It is possible to obtain precision in the attitude determination within the prescribed requirements using the UGSF with lower computational cost and with a smaller number of particles when compared to the standard PF.
William R. Silva, Roberta V. Garcia, Paula C. P. M. Pardal, H´elio K. Kuga, Maria C. F. P. S. Zanardi and Leandro Baroni have contributed equally to this work.
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© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
