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@Article{SilvaHemeLeit:2017:EsAl,
               author = "Silva, Felipe O. and Hemerly, Elder M. and Leite Filho, Waldemar 
                         de Castro",
          affiliation = "{Universidade Federal de Lavras (UFLA)} and {Instituto 
                         Tecnol{\'o}gico de Aeron{\'a}utica (ITA)} and {Instituto 
                         Nacional de Pesquisas Espaciais (INPE)}",
                title = "On the error state selection for stationary SINS alignment and 
                         calibration Kalman filters  part I: Estimation algorithms",
              journal = "Aerospace Science and Technology",
                 year = "2017",
               volume = "61",
                pages = "45--56",
                month = "Feb.",
             keywords = "Alignment, Calibration, Error state selection, Estimation, SINS.",
             abstract = "This paper presents the first part of a study aiming at error 
                         state selection in Kalman filters applied to the stationary 
                         self-alignment and calibration (SSAC) problem of strapdown 
                         inertial navigation systems (SINS). Estimation algorithms are 
                         derived through the analytical manipulation of the full SINS error 
                         model, thereby enabling us to investigate the dynamic coupling 
                         existing between the state variables. As contributions of this 
                         work, we demonstrate that the vertical velocity error is very 
                         important for the estimation of almost all error states. Latitude 
                         and altitude errors, in turn, are shown to uniquely affect the 
                         inertial sensor bias estimates. Besides, the longitude error is 
                         found to be totally detached from the system. As straightforward 
                         consequence, Bar-Itzhack and Berman's error model turns out to be 
                         inadequate for real implementations, and a 12-state Kalman filter 
                         is shown to be the optimal error state selection for SSAC 
                         purposes. Simulated and experimental tests confirm the adequacy of 
                         the outlined conclusions.",
                  doi = "10.1016/j.ast.2016.11.019",
                  url = "http://dx.doi.org/10.1016/j.ast.2016.11.019",
                 issn = "1270-9638",
             language = "en",
           targetfile = "silva_error.pdf",
        urlaccessdate = "24 nov. 2020"
}


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