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Stochastic Integration H(∞) Filter for Rapid Transfer Alignment of INS
The performance of an inertial navigation system (INS) operated on a moving base greatly depends on the accuracy of rapid transfer alignment (RTA). However, in practice, the coexistence of large initial attitude errors and uncertain observation noise statistics poses a great challenge for the estima...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2017
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5712801/ https://www.ncbi.nlm.nih.gov/pubmed/29156576 http://dx.doi.org/10.3390/s17112670 |
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author | Zhou, Dapeng Guo, Lei |
author_facet | Zhou, Dapeng Guo, Lei |
author_sort | Zhou, Dapeng |
collection | PubMed |
description | The performance of an inertial navigation system (INS) operated on a moving base greatly depends on the accuracy of rapid transfer alignment (RTA). However, in practice, the coexistence of large initial attitude errors and uncertain observation noise statistics poses a great challenge for the estimation accuracy of misalignment angles. This study aims to develop a novel robust nonlinear filter, namely the stochastic integration H [Formula: see text] filter (SIH [Formula: see text] F) for improving both the accuracy and robustness of RTA. In this new nonlinear H [Formula: see text] filter, the stochastic spherical-radial integration rule is incorporated with the framework of the derivative-free H [Formula: see text] filter for the first time, and the resulting SIH [Formula: see text] F simultaneously attenuates the negative effect in estimations caused by significant nonlinearity and large uncertainty. Comparisons between the SIH [Formula: see text] F and previously well-known methodologies are carried out by means of numerical simulation and a van test. The results demonstrate that the newly-proposed method outperforms the cubature H [Formula: see text] filter. Moreover, the SIH [Formula: see text] F inherits the benefit of the traditional stochastic integration filter, but with more robustness in the presence of uncertainty. |
format | Online Article Text |
id | pubmed-5712801 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-57128012017-12-07 Stochastic Integration H(∞) Filter for Rapid Transfer Alignment of INS Zhou, Dapeng Guo, Lei Sensors (Basel) Article The performance of an inertial navigation system (INS) operated on a moving base greatly depends on the accuracy of rapid transfer alignment (RTA). However, in practice, the coexistence of large initial attitude errors and uncertain observation noise statistics poses a great challenge for the estimation accuracy of misalignment angles. This study aims to develop a novel robust nonlinear filter, namely the stochastic integration H [Formula: see text] filter (SIH [Formula: see text] F) for improving both the accuracy and robustness of RTA. In this new nonlinear H [Formula: see text] filter, the stochastic spherical-radial integration rule is incorporated with the framework of the derivative-free H [Formula: see text] filter for the first time, and the resulting SIH [Formula: see text] F simultaneously attenuates the negative effect in estimations caused by significant nonlinearity and large uncertainty. Comparisons between the SIH [Formula: see text] F and previously well-known methodologies are carried out by means of numerical simulation and a van test. The results demonstrate that the newly-proposed method outperforms the cubature H [Formula: see text] filter. Moreover, the SIH [Formula: see text] F inherits the benefit of the traditional stochastic integration filter, but with more robustness in the presence of uncertainty. MDPI 2017-11-18 /pmc/articles/PMC5712801/ /pubmed/29156576 http://dx.doi.org/10.3390/s17112670 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 Zhou, Dapeng Guo, Lei Stochastic Integration H(∞) Filter for Rapid Transfer Alignment of INS |
title | Stochastic Integration H(∞) Filter for Rapid Transfer Alignment of INS |
title_full | Stochastic Integration H(∞) Filter for Rapid Transfer Alignment of INS |
title_fullStr | Stochastic Integration H(∞) Filter for Rapid Transfer Alignment of INS |
title_full_unstemmed | Stochastic Integration H(∞) Filter for Rapid Transfer Alignment of INS |
title_short | Stochastic Integration H(∞) Filter for Rapid Transfer Alignment of INS |
title_sort | stochastic integration h(∞) filter for rapid transfer alignment of ins |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5712801/ https://www.ncbi.nlm.nih.gov/pubmed/29156576 http://dx.doi.org/10.3390/s17112670 |
work_keys_str_mv | AT zhoudapeng stochasticintegrationhfilterforrapidtransferalignmentofins AT guolei stochasticintegrationhfilterforrapidtransferalignmentofins |