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		<doi>10.3390/s17030439</doi>
		<issn>1424-8220</issn>
		<citationkey>SilvaHemeLeit:2017:ObAn</citationkey>
		<title>On the error state selection for stationary SINS alignment and calibration Kalman filters – part II: observability/estimability analysis</title>
		<year>2017</year>
		<month>Mar.</month>
		<typeofwork>journal article</typeofwork>
		<secondarytype>PRE PI</secondarytype>
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		<author>Silva, Felipe O.,</author>
		<author>Hemerly, Elder M.,</author>
		<author>Leite Filho, Waldemar de Castro,</author>
		<group></group>
		<group></group>
		<group>DIDMC-CGETE-INPE-MCTIC-GOV-BR</group>
		<affiliation>Universidade Federal de Lavras (UFLA)</affiliation>
		<affiliation>Instituto Tecnológico de Aeronáutica (ITA)</affiliation>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<electronicmailaddress>felipe.oliveira@deg.ufla.br</electronicmailaddress>
		<electronicmailaddress>hemerly@ita.br</electronicmailaddress>
		<electronicmailaddress>waldclf@gmail.com</electronicmailaddress>
		<journal>Sensors</journal>
		<volume>17</volume>
		<number>3</number>
		<secondarymark>A1_GEOGRAFIA A1_ENGENHARIAS_IV A2_MATERIAIS A2_INTERDISCIPLINAR A2_ENGENHARIAS_I A2_CIÊNCIA_DA_COMPUTAÇÃO B1_MEDICINA_III B1_MEDICINA_II B1_MEDICINA_I B1_GEOCIÊNCIAS B1_ENGENHARIAS_III B1_ENGENHARIAS_II B1_EDUCAÇÃO_FÍSICA B1_BIODIVERSIDADE B2_QUÍMICA B2_CIÊNCIAS_BIOLÓGICAS_II B2_CIÊNCIAS_BIOLÓGICAS_I B2_BIOTECNOLOGIA B2_ASTRONOMIA_/_FÍSICA B3_MATEMÁTICA_/_PROBABILIDADE_E_ESTATÍSTICA B3_CIÊNCIAS_BIOLÓGICAS_III B4_LETRAS_/_LINGUÍSTICA C_CIÊNCIAS_AGRÁRIAS_I</secondarymark>
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		<keywords>Alignment, Calibration, Error state selection, Estimability, Observability, SINS.</keywords>
		<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.</abstract>
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		<language>en</language>
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