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dc.creatorSilva, Felipe O.-
dc.creatorHemerly, Elder M.-
dc.creatorLeite Filho, Waldemar C.-
dc.date.accessioned2018-10-03T13:52:34Z-
dc.date.available2018-10-03T13:52:34Z-
dc.date.issued2017-02-
dc.identifier.citationSILVA, F. O.; HEMERLY, E. M.; LEITE FILHO, W. C. On the error state selection for stationary SINS alignment and calibration Kalman filters – part I: estimation algorithms. Aerospace Science and Technology, [S.l.], v. 61, p. 45-56, Feb. 2017.pt_BR
dc.identifier.urihttps://www.sciencedirect.com/science/article/pii/S1270963816311397pt_BR
dc.identifier.urihttp://repositorio.ufla.br/jspui/handle/1/30881-
dc.description.abstractThis 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.pt_BR
dc.languageen_USpt_BR
dc.publisherElsevierpt_BR
dc.rightsrestrictAccesspt_BR
dc.sourceAerospace Science and Technologypt_BR
dc.subjectStrapdown Inertial Navigation Systems (SINS)pt_BR
dc.subjectAlignmentpt_BR
dc.subjectCalibrationpt_BR
dc.subjectError state selectionpt_BR
dc.subjectEstimationpt_BR
dc.titleOn the error state selection for stationary SINS alignment and calibration Kalman filters – part I: estimation algorithmspt_BR
dc.typeArtigopt_BR
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