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...
Main Authors: | , |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2017-11-01
|
Series: | Sensors |
Subjects: | |
Online Access: | https://www.mdpi.com/1424-8220/17/11/2670 |
id |
doaj-cb8636c866b64bd487e750e309cbf3a4 |
---|---|
record_format |
Article |
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 |
_version_ |
1725193347469934592 |