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1. Identity statement
Reference TypeJournal Article
Sitemtc-m21d.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34T/46L485H
Repositorysid.inpe.br/mtc-m21d/2022/04.04.14.13   (restricted access)
Last Update2022:04.04.14.13.40 (UTC) simone
Metadata Repositorysid.inpe.br/mtc-m21d/2022/04.04.14.13.40
Metadata Last Update2023:01.03.16.46.03 (UTC) administrator
DOI10.1016/j.actaastro.2021.07.049
ISSN0094-5765
Citation KeySilvaGaSaKuZaPa:2022:RaPaFi
TitleRao-Blackwellized Particle Filter for the CBERS-4 attitude and gyros bias estimation
Year2022
MonthApr.
Access Date2024, May 13
Type of Workjournal article
Secondary TypePRE PI
Number of Files1
Size4222 KiB
2. Context
Author1 Silva, William R.
2 Garcia, Roberta V.
3 Santilli, G.
4 Kuga, Hélio Koiti
5 Zanardi, M. Cecília F. P. S.
6 Pardal, Paula C. P. M.
Resume Identifier1
2
3
4 8JMKD3MGP5W/3C9JHC9
Group1
2
3
4 DIMEC-CGCE-INPE-MCTI-GOV-BR
Affiliation1 Universidade de Brasília (UnB)
2 Universidade de São Paulo (USP)
3 Universidade de Brasília (UnB)
4 Instituto Nacional de Pesquisas Espaciais (INPE)
5 Universidade Estadual Paulista (UNESP)
6 Universidade de São Paulo (USP)
Author e-Mail Address1 reis.william@unb.br
2 robertagarcia@usp.br
3 santilli@aerospace.unb.br
4 helio.kuga@inpe.br
5 cecilia@feg.unesp.br
6 paulapardal@usp.br
JournalActa Astronautica
Volume193
Number679-690
Secondary MarkA2_INTERDISCIPLINAR B1_GEOCIÊNCIAS B1_ENGENHARIAS_IV B1_ENGENHARIAS_III B2_CIÊNCIA_DA_COMPUTAÇÃO C_ENGENHARIAS_II C_ASTRONOMIA_/_FÍSICA
History (UTC)2022-04-04 14:13:40 :: simone -> administrator ::
2022-04-04 14:13:41 :: administrator -> simone :: 2022
2022-04-04 14:14:35 :: simone -> administrator :: 2022
2022-07-08 16:51:14 :: administrator -> simone :: 2022
2022-12-19 19:21:08 :: simone -> administrator :: 2022
2023-01-03 16:46:03 :: administrator -> simone :: 2022
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsArtificial satellites
Attitude estimation
CBERS-4
Gyros bias
Rao-Blackwellized Particle Filter
Unscented Kalman Filter
AbstractThe Rao-Blackwellized Particle Filter (RaoBPF) and the Unscented Kalman Filter (UKF) were applied in this work to attitude and gyros bias estimation using simulated orbit and attitude measurement data for CBERS-4 (China Brazil Earth Resources Satellite 4) recently in operation. CBERS-4 was launched in 2014, controlled and operated in shifts by China (Xi'an Control Center) and Brazil (Satellite Control Center). Its orbit is heliosynchronous with an inclination of 98.504 degrees, a semi-major axis of 7148.865 km, eccentricity 1.1×10−3, crossing equador line at 10h30min in a descending direction with perigee frozen at 90 degrees, which establishes a commitment relationship between a satisfactory amount of solar irradiance, contrast between targets, and the presence of clouds. This configuration provides global coverage every 26 days. The real orbit and attitude measurements were provided by the Satellite Control Center of the National Institute for Space Research (CCS - INPE) from September 1st, 2015. The dynamic attitude model is described by quaternions. The available attitude sensors are two Digital Sun Sensors (DSS), two Infrared Earth Sensor (IRES) and a triad of mechanical gyroscopes. The two IRES give direct measurements of roll and pitch angles with a certain level of error. The two DSS are nonlinear functions of roll, pitch, and yaw attitude angles. The gyros furnish the angular measurements in the body frame reference system. Gyros provide direct incremental angles or angular velocities; however, they present several sources of error, and the drift is the most troublesome. Such drifts yield along time an accumulation of errors which must be accounted in the attitude determination process. The RaoBPF estimation method used to attitude and gyros bias estimation is a technique that exploits the state space structure in order to reduce the number of particles, decreasing the processing time, avoiding the computational effort common to the standard particle filter. The logical extension of the RaoBPF provides a more general model that can be divided into purely non-linear and conditionally linear-Gaussian aspects, which explores this structure, marginalizing the conditional linear parts and estimating them using exact filters, such as the Extended Kalman Filter (EKF). The results show that it is possible to achieve precision in determining attitudes within the prescribed requirements using the RaoBPF, with lower computational cost when compared to the standard particle filter and its branches, in addition to have competitive results such as the UKF.
AreaETES
ArrangementRao-Blackwellized Particle Filter...
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4. Conditions of access and use
Languageen
Target FileSilva_2022_rao.pdf
User Groupsimone
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Visibilityshown
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5. Allied materials
Next Higher Units8JMKD3MGPCW/46KTFK8
Citing Item Listsid.inpe.br/bibdigital/2022/04.03.17.52 2
sid.inpe.br/mtc-m21/2012/07.13.14.49.46 2
DisseminationWEBSCI; PORTALCAPES; COMPENDEX.
Host Collectionurlib.net/www/2021/06.04.03.40
6. Notes
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