Online auto-calibration of triaxial accelerometer with time-variant model structures

Ye, L; Argha, A; Celler, BG; Nguyen, HT; Su, SW

Online auto-calibration of triaxial accelerometer with time-variant model structures

Ye, L Argha, A

Celler, BG Nguyen, HT

Su, SW

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Publication Type:: Journal Article
Citation:: Sensors and Actuators, A: Physical, 2017, 266 pp. 294 - 307
Issue Date:: 2017-10-15

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Field	Value	Language
dc.contributor.author	Ye, L	en_US
dc.contributor.author	Argha, A https://orcid.org/0000-0002-8276-9774	en_US
dc.contributor.author	Celler, BG	en_US
dc.contributor.author	Nguyen, HT https://orcid.org/0000-0003-3373-8178	en_US
dc.contributor.author	Su, SW https://orcid.org/0000-0002-5720-8852	en_US
dc.date.available	2020-05-25T19:08:56Z
dc.date.issued	2017-10-15	en_US
dc.identifier.citation	Sensors and Actuators, A: Physical, 2017, 266 pp. 294 - 307	en_US
dc.identifier.issn	0924-4247	en_US
dc.identifier.uri	http://hdl.handle.net/10453/120578
dc.description.abstract	© 2017 Elsevier B.V. In this paper, an online auto-calibration method for MicroElectroMechanical Systems (MEMS) triaxial accelerometer (TA) is proposed, which can simultaneously identify the time-dependent model structure and its parameters during the changes of the operating environment. Firstly, the model as well as its associated cost function is linearized by a new proposed linearization approach. Then, exploiting an online sparse recursive least square (SPARLS) estimation, the unknown parameters are identified. In particular, the online sparse recursive method is based on an L1-norm penalized expectation-maximum (EM) algorithm, which can amend the model automatically by penalizing the insignificant parameters to zero. Furthermore, this method can reduce computational complexity and be implemented in a low-cost Micro-Controller-Unit (MCU). Based on the numerical analysis, it can be concluded that the proposed recursive algorithm can calculate the unknown parameters reliably and accurately for most MEMS triaxial accelerometers available in the market. Additionally, this method is experimentally validated by comparing the output estimations before and after calibration under various scenarios, which further confirms its feasibility and effectiveness for online TA calibration.	en_US
dc.relation.ispartof	Sensors and Actuators, A: Physical	en_US
dc.relation.isbasedon	10.1016/j.sna.2017.08.049	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	Nanoscience & Nanotechnology	en_US
dc.title	Online auto-calibration of triaxial accelerometer with time-variant model structures	en_US
dc.type	Journal Article
utslib.citation.volume	266	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0912 Materials Engineering	en_US
utslib.for	0913 Mechanical 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	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	266	en_US

Abstract:

© 2017 Elsevier B.V. In this paper, an online auto-calibration method for MicroElectroMechanical Systems (MEMS) triaxial accelerometer (TA) is proposed, which can simultaneously identify the time-dependent model structure and its parameters during the changes of the operating environment. Firstly, the model as well as its associated cost function is linearized by a new proposed linearization approach. Then, exploiting an online sparse recursive least square (SPARLS) estimation, the unknown parameters are identified. In particular, the online sparse recursive method is based on an L1-norm penalized expectation-maximum (EM) algorithm, which can amend the model automatically by penalizing the insignificant parameters to zero. Furthermore, this method can reduce computational complexity and be implemented in a low-cost Micro-Controller-Unit (MCU). Based on the numerical analysis, it can be concluded that the proposed recursive algorithm can calculate the unknown parameters reliably and accurately for most MEMS triaxial accelerometers available in the market. Additionally, this method is experimentally validated by comparing the output estimations before and after calibration under various scenarios, which further confirms its feasibility and effectiveness for online TA calibration.

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http://hdl.handle.net/10453/120578