Observability Analysis of a Matrix Kalman Filter-Based Navigation System Using Visual/Inertial/Magnetic Sensors

A matrix Kalman filter (MKF) has been implemented for an integrated navigation system using visual/inertial/magnetic sensors. The MKF rearranges the original nonlinear process model in a pseudo-linear process model. We employ the observability rank criterion based on Lie derivatives to verify the co...

Descripción completa

Detalles Bibliográficos
Autores principales: Feng, Guohu, Wu, Wenqi, Wang, Jinling
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Molecular Diversity Preservation International (MDPI) 2012
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3444081/
https://www.ncbi.nlm.nih.gov/pubmed/23012523
http://dx.doi.org/10.3390/s120708877
_version_ 1782243629261127680
author Feng, Guohu
Wu, Wenqi
Wang, Jinling
author_facet Feng, Guohu
Wu, Wenqi
Wang, Jinling
author_sort Feng, Guohu
collection PubMed
description A matrix Kalman filter (MKF) has been implemented for an integrated navigation system using visual/inertial/magnetic sensors. The MKF rearranges the original nonlinear process model in a pseudo-linear process model. We employ the observability rank criterion based on Lie derivatives to verify the conditions under which the nonlinear system is observable. It has been proved that such observability conditions are: (a) at least one degree of rotational freedom is excited, and (b) at least two linearly independent horizontal lines and one vertical line are observed. Experimental results have validated the correctness of these observability conditions.
format Online
Article
Text
id pubmed-3444081
institution National Center for Biotechnology Information
language English
publishDate 2012
publisher Molecular Diversity Preservation International (MDPI)
record_format MEDLINE/PubMed
spelling pubmed-34440812012-09-25 Observability Analysis of a Matrix Kalman Filter-Based Navigation System Using Visual/Inertial/Magnetic Sensors Feng, Guohu Wu, Wenqi Wang, Jinling Sensors (Basel) Article A matrix Kalman filter (MKF) has been implemented for an integrated navigation system using visual/inertial/magnetic sensors. The MKF rearranges the original nonlinear process model in a pseudo-linear process model. We employ the observability rank criterion based on Lie derivatives to verify the conditions under which the nonlinear system is observable. It has been proved that such observability conditions are: (a) at least one degree of rotational freedom is excited, and (b) at least two linearly independent horizontal lines and one vertical line are observed. Experimental results have validated the correctness of these observability conditions. Molecular Diversity Preservation International (MDPI) 2012-06-27 /pmc/articles/PMC3444081/ /pubmed/23012523 http://dx.doi.org/10.3390/s120708877 Text en © 2012 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution license (http://creativecommons.org/licenses/by/3.0/).
spellingShingle Article
Feng, Guohu
Wu, Wenqi
Wang, Jinling
Observability Analysis of a Matrix Kalman Filter-Based Navigation System Using Visual/Inertial/Magnetic Sensors
title Observability Analysis of a Matrix Kalman Filter-Based Navigation System Using Visual/Inertial/Magnetic Sensors
title_full Observability Analysis of a Matrix Kalman Filter-Based Navigation System Using Visual/Inertial/Magnetic Sensors
title_fullStr Observability Analysis of a Matrix Kalman Filter-Based Navigation System Using Visual/Inertial/Magnetic Sensors
title_full_unstemmed Observability Analysis of a Matrix Kalman Filter-Based Navigation System Using Visual/Inertial/Magnetic Sensors
title_short Observability Analysis of a Matrix Kalman Filter-Based Navigation System Using Visual/Inertial/Magnetic Sensors
title_sort observability analysis of a matrix kalman filter-based navigation system using visual/inertial/magnetic sensors
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3444081/
https://www.ncbi.nlm.nih.gov/pubmed/23012523
http://dx.doi.org/10.3390/s120708877
work_keys_str_mv AT fengguohu observabilityanalysisofamatrixkalmanfilterbasednavigationsystemusingvisualinertialmagneticsensors
AT wuwenqi observabilityanalysisofamatrixkalmanfilterbasednavigationsystemusingvisualinertialmagneticsensors
AT wangjinling observabilityanalysisofamatrixkalmanfilterbasednavigationsystemusingvisualinertialmagneticsensors

Ejemplares similares