Performance analysis of derivative-free estimation methods from the perspective of attitude estimation influenced by real data

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Autor(es): dc.contributorUniversidade de São Paulo (USP)-
Autor(es): dc.contributorNational Institute for Space Research (INPE)-
Autor(es): dc.contributorUniversity of Brasilia (UnB)-
Autor(es): dc.contributorUniversidade Federal do ABC (UFABC)-
Autor(es): dc.contributorUniversidade Estadual Paulista (UNESP)-
Autor(es): dc.contributorPACT-
Autor(es): dc.creatorGarcia, Roberta V.-
Autor(es): dc.creatorKuga, Hélio K.-
Autor(es): dc.creatorSilva, William R.-
Autor(es): dc.creatorBaroni, Leandro-
Autor(es): dc.creatorZanardi, Maria C. F. P. S.-
Autor(es): dc.creatorPardal, Paula C. P. M.-
Data de aceite: dc.date.accessioned2025-08-21T21:18:05Z-
Data de disponibilização: dc.date.available2025-08-21T21:18:05Z-
Data de envio: dc.date.issued2025-04-29-
Data de envio: dc.date.issued2023-11-30-
Fonte completa do material: dc.identifierhttp://dx.doi.org/10.1140/epjs/s11734-023-01014-0-
Fonte completa do material: dc.identifierhttps://hdl.handle.net/11449/299691-
Fonte: dc.identifier.urihttp://educapes.capes.gov.br/handle/11449/299691-
Descrição: dc.descriptionThe main difference between the Extended Kalman Filter (EKF) and the non-linear estimators that make use of the so-called Sigma Points is the need or not of linearizing the equations that compose the whole dynamic system, a process that requires the calculation of the Jacobian matrices composed of partial derivatives. In this study, an analysis of the Central Difference Kalman Filter (CDKF) efficiency is performed, as compared to other derivative-free estimators and the standard EKF, when real data from on-board satellite sensors are processed by the filters. However, the use of real data can generate problems, not only regarding errors and uncertainties of different natures that can lead the filter to inaccurate results, but also regarding the difficulty in validating the results due to the absence of reference values. In this case, results of the attitude estimated by filters, such as EKF, Unscented Kalman Filter (UKF), and Cubature Kalman Filter (CKF), already validated in previous papers served as the basis for the comparisons made with the CDKF. It was observed that the performance of CDKF is superior to the conventional EKF and equivalent to filters that make use of the sigma points, while still maintaining an adequate processing time for real applications.-
Descrição: dc.descriptionLorena School of Engineering (EEL) University of São Paulo (USP), Estrada Municipal do Campinho, S/N. Ponte Nova, São Paulo-
Descrição: dc.descriptionSpace Mechanics and Control Division (DMC) National Institute for Space Research (INPE), Av Dos Astronautas, 1758, Jd. da Granja, São Paulo-
Descrição: dc.descriptionGama Campus (FGA) University of Brasilia (UnB), Área Especial de Indústria, Projeção A, Setor Leste (Gama), Federal District-
Descrição: dc.descriptionEngineering Modeling and Applied Social Sciences Center (CECS) Federal University of ABC (UFABC), Av. dos Estados, 5001, Bangú, São Paulo-
Descrição: dc.descriptionMathematics Department (DM) São Paulo University (UNESP), Av. Ariberto Pereira da Cunha, 333, Pedregulho, São Paulo-
Descrição: dc.descriptionCollaborative Laboratory (CoLAB) Center of Engineering and Product Development (CEiiA) PACT, Rua Luís Adelino Fonseca, 1-
Descrição: dc.descriptionMathematics Department (DM) São Paulo University (UNESP), Av. Ariberto Pereira da Cunha, 333, Pedregulho, São Paulo-
Formato: dc.format2937-2948-
Idioma: dc.languageen-
Relação: dc.relationEuropean Physical Journal: Special Topics-
???dc.source???: dc.sourceScopus-
Título: dc.titlePerformance analysis of derivative-free estimation methods from the perspective of attitude estimation influenced by real data-
Tipo de arquivo: dc.typelivro digital-
Aparece nas coleções:Repositório Institucional - Unesp

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