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...

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Main Authors: Dapeng Zhou, Lei Guo
Format: Article
Language:English
Published: MDPI AG 2017-11-01
Series:Sensors
Subjects:
Online Access:https://www.mdpi.com/1424-8220/17/11/2670
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spelling doaj-cb8636c866b64bd487e750e309cbf3a42020-11-25T01:05:46ZengMDPI AGSensors1424-82202017-11-011711267010.3390/s17112670s17112670Stochastic Integration H∞ Filter for Rapid Transfer Alignment of INSDapeng Zhou0Lei Guo1School of Instrumentation Science and Opto-Electronics Engineering, Beihang University, Beijing 100191, ChinaScience and Technology on Aircraft Control Laboratory, Beihang University, Beijing 100191, ChinaThe 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 ∞ filter (SIH ∞ F) for improving both the accuracy and robustness of RTA. In this new nonlinear H ∞ filter, the stochastic spherical-radial integration rule is incorporated with the framework of the derivative-free H ∞ filter for the first time, and the resulting SIH ∞ F simultaneously attenuates the negative effect in estimations caused by significant nonlinearity and large uncertainty. Comparisons between the SIH ∞ 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 ∞ filter. Moreover, the SIH ∞ F inherits the benefit of the traditional stochastic integration filter, but with more robustness in the presence of uncertainty.https://www.mdpi.com/1424-8220/17/11/2670inertial navigation systemrapid transfer alignmentstochastic integration H∞ filter
collection DOAJ
language English
format Article
sources DOAJ
author Dapeng Zhou
Lei Guo
spellingShingle Dapeng Zhou
Lei Guo
Stochastic Integration H∞ Filter for Rapid Transfer Alignment of INS
Sensors
inertial navigation system
rapid transfer alignment
stochastic integration H∞ filter
author_facet Dapeng Zhou
Lei Guo
author_sort Dapeng Zhou
title Stochastic Integration H∞ Filter for Rapid Transfer Alignment of INS
title_short 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_sort stochastic integration h∞ filter for rapid transfer alignment of ins
publisher MDPI AG
series Sensors
issn 1424-8220
publishDate 2017-11-01
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 ∞ filter (SIH ∞ F) for improving both the accuracy and robustness of RTA. In this new nonlinear H ∞ filter, the stochastic spherical-radial integration rule is incorporated with the framework of the derivative-free H ∞ filter for the first time, and the resulting SIH ∞ F simultaneously attenuates the negative effect in estimations caused by significant nonlinearity and large uncertainty. Comparisons between the SIH ∞ 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 ∞ filter. Moreover, the SIH ∞ F inherits the benefit of the traditional stochastic integration filter, but with more robustness in the presence of uncertainty.
topic inertial navigation system
rapid transfer alignment
stochastic integration H∞ filter
url https://www.mdpi.com/1424-8220/17/11/2670
work_keys_str_mv AT dapengzhou stochasticintegrationhfilterforrapidtransferalignmentofins
AT leiguo stochasticintegrationhfilterforrapidtransferalignmentofins
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