A fast-converge, real-time auto-calibration algorithm for triaxial accelerometer

Ye, L; Guo, Y; Dong, L; Yu, H; Nguyen, H; Su, SW

A fast-converge, real-time auto-calibration algorithm for triaxial accelerometer

Ye, L Guo, Y Dong, L Yu, H

Nguyen, H Su, SW

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Publication Type:: Journal Article
Citation:: Measurement Science and Technology, 2019, 30 (6)
Issue Date:: 2019-05-10

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Field	Value	Language
dc.contributor.author	Ye, L	en_US
dc.contributor.author	Guo, Y	en_US
dc.contributor.author	Dong, L	en_US
dc.contributor.author	Yu, H https://orcid.org/0000-0003-3018-064X	en_US
dc.contributor.author	Nguyen, H	en_US
dc.contributor.author	Su, SW https://orcid.org/0000-0002-5720-8852	en_US
dc.date.issued	2019-05-10	en_US
dc.identifier.citation	Measurement Science and Technology, 2019, 30 (6)	en_US
dc.identifier.issn	0957-0233	en_US
dc.identifier.uri	http://hdl.handle.net/10453/134765
dc.description.abstract	© 2019 IOP Publishing Ltd. Online parameter estimation for nonlinear systems are challenging, especially when only limited computational powers are available. The auto-calibration of triaxial accelerometers is essentially a nonlinear parameter estimation problem. In this paper, a simple but efficient auto-calibration method for micro triaxial accelerometers is proposed. In particular, this method is an online calibration approach based on the six-parameter auto-calibration model, which can be implemented in a wearable health-monitoring device to compensate the estimation errors due to parameter drift. To achieve online calibration, the nonlinear model together with its cost function for auto-calibration is linearized, and an online recursive method is exploited to identify the unknown parameters and remove the bias caused by linearization. This online recursive method is developed by modifying the damped recursive least square estimation (MDRLS), where the estimated unknown parameter can converge stably in a short period. The MDRLS-based method can significantly reduce the calculation complexity comparing to the commonly used nonlinear optimization method. Additionally, the proposed method is validated by both simulation and experiment. The simulation results show the estimated parameters are very close to their true values. By comparing the output results before and after calibration in different experimental conditions, the effectiveness of the proposed method is further verified by real-time experiments.	en_US
dc.relation.ispartof	Measurement Science and Technology	en_US
dc.relation.isbasedon	10.1088/1361-6501/ab08c9	en_US
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Optics	en_US
dc.title	A fast-converge, real-time auto-calibration algorithm for triaxial accelerometer	en_US
dc.type	Journal Article
utslib.citation.volume	6	en_US
utslib.citation.volume	30	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	02 Physical Sciences	en_US
utslib.for	09 Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Biomedical Engineering
pubs.organisational-group	/University of Technology Sydney/Strength - CHT - Health Technologies
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	closed_access	*
pubs.issue	6	en_US
pubs.publication-status	Published	en_US
pubs.volume	30	en_US

Abstract:

© 2019 IOP Publishing Ltd. Online parameter estimation for nonlinear systems are challenging, especially when only limited computational powers are available. The auto-calibration of triaxial accelerometers is essentially a nonlinear parameter estimation problem. In this paper, a simple but efficient auto-calibration method for micro triaxial accelerometers is proposed. In particular, this method is an online calibration approach based on the six-parameter auto-calibration model, which can be implemented in a wearable health-monitoring device to compensate the estimation errors due to parameter drift. To achieve online calibration, the nonlinear model together with its cost function for auto-calibration is linearized, and an online recursive method is exploited to identify the unknown parameters and remove the bias caused by linearization. This online recursive method is developed by modifying the damped recursive least square estimation (MDRLS), where the estimated unknown parameter can converge stably in a short period. The MDRLS-based method can significantly reduce the calculation complexity comparing to the commonly used nonlinear optimization method. Additionally, the proposed method is validated by both simulation and experiment. The simulation results show the estimated parameters are very close to their true values. By comparing the output results before and after calibration in different experimental conditions, the effectiveness of the proposed method is further verified by real-time experiments.

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http://hdl.handle.net/10453/134765