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A Kalman Filter for SINS Self-Alignment Based on Vector Observation

In this paper, a self-alignment method for strapdown inertial navigation systems based on the q-method is studied. In addition, an improved method based on integrating gravitational apparent motion to form apparent velocity is designed, which can reduce the random noises of the observation vectors....

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Detalles Bibliográficos
Autores principales: Xu, Xiang, Xu, Xiaosu, Zhang, Tao, Li, Yao, Tong, Jinwu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5336054/
https://www.ncbi.nlm.nih.gov/pubmed/28146059
http://dx.doi.org/10.3390/s17020264
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author Xu, Xiang
Xu, Xiaosu
Zhang, Tao
Li, Yao
Tong, Jinwu
author_facet Xu, Xiang
Xu, Xiaosu
Zhang, Tao
Li, Yao
Tong, Jinwu
author_sort Xu, Xiang
collection PubMed
description In this paper, a self-alignment method for strapdown inertial navigation systems based on the q-method is studied. In addition, an improved method based on integrating gravitational apparent motion to form apparent velocity is designed, which can reduce the random noises of the observation vectors. For further analysis, a novel self-alignment method using a Kalman filter based on adaptive filter technology is proposed, which transforms the self-alignment procedure into an attitude estimation using the observation vectors. In the proposed method, a linear psuedo-measurement equation is adopted by employing the transfer method between the quaternion and the observation vectors. Analysis and simulation indicate that the accuracy of the self-alignment is improved. Meanwhile, to improve the convergence rate of the proposed method, a new method based on parameter recognition and a reconstruction algorithm for apparent gravitation is devised, which can reduce the influence of the random noises of the observation vectors. Simulations and turntable tests are carried out, and the results indicate that the proposed method can acquire sound alignment results with lower standard variances, and can obtain higher alignment accuracy and a faster convergence rate.
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spelling pubmed-53360542017-03-16 A Kalman Filter for SINS Self-Alignment Based on Vector Observation Xu, Xiang Xu, Xiaosu Zhang, Tao Li, Yao Tong, Jinwu Sensors (Basel) Article In this paper, a self-alignment method for strapdown inertial navigation systems based on the q-method is studied. In addition, an improved method based on integrating gravitational apparent motion to form apparent velocity is designed, which can reduce the random noises of the observation vectors. For further analysis, a novel self-alignment method using a Kalman filter based on adaptive filter technology is proposed, which transforms the self-alignment procedure into an attitude estimation using the observation vectors. In the proposed method, a linear psuedo-measurement equation is adopted by employing the transfer method between the quaternion and the observation vectors. Analysis and simulation indicate that the accuracy of the self-alignment is improved. Meanwhile, to improve the convergence rate of the proposed method, a new method based on parameter recognition and a reconstruction algorithm for apparent gravitation is devised, which can reduce the influence of the random noises of the observation vectors. Simulations and turntable tests are carried out, and the results indicate that the proposed method can acquire sound alignment results with lower standard variances, and can obtain higher alignment accuracy and a faster convergence rate. MDPI 2017-01-29 /pmc/articles/PMC5336054/ /pubmed/28146059 http://dx.doi.org/10.3390/s17020264 Text en © 2017 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 (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Xu, Xiang
Xu, Xiaosu
Zhang, Tao
Li, Yao
Tong, Jinwu
A Kalman Filter for SINS Self-Alignment Based on Vector Observation
title A Kalman Filter for SINS Self-Alignment Based on Vector Observation
title_full A Kalman Filter for SINS Self-Alignment Based on Vector Observation
title_fullStr A Kalman Filter for SINS Self-Alignment Based on Vector Observation
title_full_unstemmed A Kalman Filter for SINS Self-Alignment Based on Vector Observation
title_short A Kalman Filter for SINS Self-Alignment Based on Vector Observation
title_sort kalman filter for sins self-alignment based on vector observation
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5336054/
https://www.ncbi.nlm.nih.gov/pubmed/28146059
http://dx.doi.org/10.3390/s17020264
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