Multivariate versus Univariate Sensor Selection for Spatial Field Estimation

Nguyen, L; Thiyagarajan, K; Ulapane, N; Kodagoda, S

Multivariate versus Univariate Sensor Selection for Spatial Field Estimation

Nguyen, L Thiyagarajan, K

Ulapane, N Kodagoda, S

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: 2021 IEEE 16th Conference on Industrial Electronics and Applications (ICIEA), 2021, 00, pp. 1187-1192
Issue Date:: 2021-08-01

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Field	Value	Language
dc.contributor.author	Nguyen, L
dc.contributor.author	Thiyagarajan, K https://orcid.org/0000-0002-4044-1711
dc.contributor.author	Ulapane, N
dc.contributor.author	Kodagoda, S https://orcid.org/0000-0001-5175-9138
dc.date	2021-08-01
dc.date.accessioned	2021-12-27T10:28:03Z
dc.date.available	2021-12-27T10:28:03Z
dc.date.issued	2021-08-01
dc.identifier.citation	2021 IEEE 16th Conference on Industrial Electronics and Applications (ICIEA), 2021, 00, pp. 1187-1192
dc.identifier.isbn	9781665422482
dc.identifier.uri	http://hdl.handle.net/10453/152529
dc.description.abstract	The paper discusses the sensor selection problem in estimating spatial fields. It is demonstrated that selecting a subset of sensors depends on modelling spatial processes. It is first proposed to exploit Gaussian process (GP) to model a univariate spatial field and multivariate GP (MGP) to jointly represent multivariate spatial phenomena. A Matérn cross-covariance function is employed in the MGP model to guarantee its cross-covariance matrices to be positive semi-definite. We then consider two corresponding univariate and multivariate sensor selection problems in effectively monitoring multiple spatial random fields. The sensor selection approaches were implemented in the real-world experiments and their performances were compared. Difference of results obtained by the univariate and multivariate sensor selection techniques is insignificant; that is, either of the methods can be efficiently used in practice.
dc.language	en
dc.publisher	IEEE
dc.relation.ispartof	2021 IEEE 16th Conference on Industrial Electronics and Applications (ICIEA)
dc.relation.ispartof	2021 IEEE 16th Conference on Industrial Electronics and Applications
dc.relation.hasversion	Accepted Manuscript Version	en
dc.relation.isbasedon	10.1109/iciea51954.2021.9516093
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.title	Multivariate versus Univariate Sensor Selection for Spatial Field Estimation
dc.type	Conference Proceeding
utslib.citation.volume	00
utslib.location.activity	Chengdu, China
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/Strength - CAS - Centre for Autonomous Systems
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Mechanical and Mechatronic Engineering
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2023-08-30T00:00:00+1000Z
dc.date.updated	2021-12-27T10:28:02Z
pubs.finish-date	2021-08-04
pubs.publication-status	Published
pubs.start-date	2021-08-01
pubs.volume	00

Abstract:

The paper discusses the sensor selection problem in estimating spatial fields. It is demonstrated that selecting a subset of sensors depends on modelling spatial processes. It is first proposed to exploit Gaussian process (GP) to model a univariate spatial field and multivariate GP (MGP) to jointly represent multivariate spatial phenomena. A Matérn cross-covariance function is employed in the MGP model to guarantee its cross-covariance matrices to be positive semi-definite. We then consider two corresponding univariate and multivariate sensor selection problems in effectively monitoring multiple spatial random fields. The sensor selection approaches were implemented in the real-world experiments and their performances were compared. Difference of results obtained by the univariate and multivariate sensor selection techniques is insignificant; that is, either of the methods can be efficiently used in practice.

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http://hdl.handle.net/10453/152529