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@Article{SilvaHemeLeit:2017:ObAn,
               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 II: observability/estimability 
                         analysis",
              journal = "Sensors",
                 year = "2017",
               volume = "17",
               number = "3",
                month = "Mar.",
             keywords = "Alignment, Calibration, Error state selection, Estimability, 
                         Observability, SINS.",
             abstract = "This paper presents the second part of a study aiming at the error 
                         state selection in Kalman filters applied to the stationary 
                         self-alignment and calibration (SSAC) problem of strapdown 
                         inertial navigation systems (SINS). The observability properties 
                         of the system are systematically investigated, and the number of 
                         unobservable modes is established. Through the analytical 
                         manipulation of the full SINS error model, the unobservable modes 
                         of the system are determined, and the SSAC error states (except 
                         the velocity errors) are proven to be individually unobservable. 
                         The estimability of the system is determined through the 
                         examination of the major diagonal terms of the covariance matrix 
                         and their eigenvalues/eigenvectors. Filter order reduction based 
                         on observability analysis is shown to be inadequate, and several 
                         misconceptions regarding SSAC observability and estimability 
                         deficiencies are removed. As the main contributions of this paper, 
                         we demonstrate that, except for the position errors, all error 
                         states can be minimally estimated in the SSAC problem and, hence, 
                         should not be removed from the filter. Corroborating the 
                         conclusions of the first part of this study, a 12-state Kalman 
                         filter is found to be the optimal error state selection for SSAC 
                         purposes. Results from simulated and experimental tests support 
                         the outlined conclusions.",
                  doi = "10.3390/s17030439",
                  url = "http://dx.doi.org/10.3390/s17030439",
                 issn = "1424-8220",
             language = "en",
           targetfile = "silva_ontheerror2.pdf",
        urlaccessdate = "24 nov. 2020"
}


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