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@Article{GarciaKugaZana:2012:UnKaFi,
               author = "Garcia, Roberta Veloso and Kuga, Helio Koiti and Zanardi, Maria 
                         Cecilia F. P. S.",
          affiliation = "{} and {Instituto Nacional de Pesquisas Espaciais (INPE)} and 
                         Department of Mathematics, FEG, UNESP",
                title = "Unscented Kalman Filter Applied to the Spacecraft Attitude 
                         Estimation with Euler Angles",
              journal = "Mathematical Problems in Engineering",
                 year = "2012",
               volume = "2012",
               number = "Article ID 985429",
                pages = "1--12",
                 note = "{Setores de Atividade: Pesquisa e desenvolvimento 
                         cient{\'{\i}}fico.}",
             keywords = "Unscented Kalman filter, ATTITUDE DETERMINATION, Euler angles 
                         estimation.",
             abstract = "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.",
                  doi = "10.1155/2012/985429",
                  url = "http://dx.doi.org/10.1155/2012/985429",
                 issn = "1024-123X and 1563-5147",
                label = "lattes: 1786255724025154 2 GarciaKugaZana:2012:UnKaFi",
             language = "en",
           targetfile = "Garcia_et_al_2012_UnscentedKalmanFilter.pdf",
        urlaccessdate = "23 jan. 2021"
}


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