An online recursive autocalibration of triaxial accelerometer

Ye, L; Su, SW; Lei, D; Nguyen, HT

An online recursive autocalibration of triaxial accelerometer

Ye, L Su, SW

Lei, D Nguyen, HT

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Publication Type:: Conference Proceeding
Citation:: Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2016, 2016-October pp. 2038 - 2041
Issue Date:: 2016-10-13

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Field	Value	Language
dc.contributor.author	Ye, L	en_US
dc.contributor.author	Su, SW https://orcid.org/0000-0002-5720-8852	en_US
dc.contributor.author	Lei, D	en_US
dc.contributor.author	Nguyen, HT https://orcid.org/0000-0003-3373-8178	en_US
dc.date.issued	2016-10-13	en_US
dc.identifier.citation	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS, 2016, 2016-October pp. 2038 - 2041	en_US
dc.identifier.isbn	9781457702204	en_US
dc.identifier.issn	1557-170X	en_US
dc.identifier.uri	http://hdl.handle.net/10453/80223
dc.description.abstract	© 2016 IEEE. In this paper, we proposed a novel method for autocalibration of triaxial Micro-Electro-Mechanical systems (MEMS) accelerometer that does not require any sophisticated laboratory facilities. In particular, this method is an online calibration method which can be conveniently implemented with the accuracy of MEMS accelerometer being significantly improved. The procedure exploits the fact that the output vector of the accelerometer must match the local gravity in static state condition. To achieve online calibration, the model as well as the cost function are linearized at the beginning, and an online recursive method is then utilized to identify the unknown parameters and remove the bias caused by linearization. This online recursive method is based on damped recursive least square estimation (DRLS), which can significantly reduce the calculation complexity comparing to nonlinear optimization method. In addition, the unknown parameters can be solved in a short time and the estimated parameters can remain stable during calibration. Experimentally, this method was tested by comparing the output results before and after calibration in different condition. It showed that the output, after calibrated by the proposed method, is more accurate with respect to raw output using default factory parameters.	en_US
dc.relation.ispartof	Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS	en_US
dc.relation.isbasedon	10.1109/EMBC.2016.7591127	en_US
dc.subject.mesh	Calibration	en_US
dc.subject.mesh	Least-Squares Analysis	en_US
dc.subject.mesh	Algorithms	en_US
dc.subject.mesh	Gravitation	en_US
dc.subject.mesh	Micro-Electrical-Mechanical Systems	en_US
dc.title	An online recursive autocalibration of triaxial accelerometer	en_US
dc.type	Conference Proceeding
utslib.citation.volume	2016-October	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0903 Biomedical 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.publication-status	Published	en_US
pubs.volume	2016-October	en_US

Abstract:

© 2016 IEEE. In this paper, we proposed a novel method for autocalibration of triaxial Micro-Electro-Mechanical systems (MEMS) accelerometer that does not require any sophisticated laboratory facilities. In particular, this method is an online calibration method which can be conveniently implemented with the accuracy of MEMS accelerometer being significantly improved. The procedure exploits the fact that the output vector of the accelerometer must match the local gravity in static state condition. To achieve online calibration, the model as well as the cost function are linearized at the beginning, and an online recursive method is then utilized to identify the unknown parameters and remove the bias caused by linearization. This online recursive method is based on damped recursive least square estimation (DRLS), which can significantly reduce the calculation complexity comparing to nonlinear optimization method. In addition, the unknown parameters can be solved in a short time and the estimated parameters can remain stable during calibration. Experimentally, this method was tested by comparing the output results before and after calibration in different condition. It showed that the output, after calibrated by the proposed method, is more accurate with respect to raw output using default factory parameters.

Please use this identifier to cite or link to this item:

http://hdl.handle.net/10453/80223