- Title
- Multivariate versus univariate sensor selection for spatial field estimation
- Creator
- Nguyen, Linh; Thiyagarajan, Karthick; Ulapane, Nalika; Kodagoda, Sarath
- Date
- 2021
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/180268
- Identifier
- vital:15741
- Identifier
-
https://doi.org/10.1109/ICIEA51954.2021.9516093
- Identifier
- ISBN:9781665422482 (ISBN)
- 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. © 2021 IEEE.
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- 16th IEEE Conference on Industrial Electronics and Applications, ICIEA 2021 p. 1187-1192
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright @ 2021 IEEE
- Subject
- Gaussian process; Multimodal sensing; Multivariable; Multivariate; Sensor selection; Spatial fields; Univariate
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