- Title
- Multimodal sensor selection for multiple spatial field reconstruction
- Creator
- Nguyen, Linh; Thiyagarajan, Karthick; Ulapane, Nalika; Kodagoda, Sarath
- Date
- 2021
- Type
- Text; Conference paper
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/180353
- Identifier
- vital:15740
- Identifier
-
https://doi.org/10.1109/ICIEA51954.2021.9516255
- Identifier
- ISBN:9781665422482 (ISBN)
- Abstract
- The paper addresses the multimodal sensor selection problem where selected colocated sensor nodes are employed to effectively monitor and efficiently predict multiple spatial random fields. It is first proposed to exploit multivariate Gaussian processes (MGP) to model multiple spatial phenomena jointly. By the use of the Matérn cross-covariance function, cross-covariance matrices in the MGP model are sufficiently positive semi-definite, concomitantly providing efficient prediction of all multivariate processes at unmeasured locations. The multimodal sensor selection problem is then formulated and solved by an approximate algorithm with an aim to select the most informative sensor nodes so that prediction uncertainties at all the fields are minimized. The proposed approach was validated in the real-life experiments with promising results. © 2021 IEEE.
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Relation
- 16th IEEE Conference on Industrial Electronics and Applications, ICIEA 2021 p. 1181-1186
- Rights
- All metadata describing materials held in, or linked to, the repository is freely available under a CC0 licence
- Rights
- Copyright @ 2021 IEEE
- Subject
- Multimodal sensing; Multiple spatial fields; Multivariable; Multivariate; Multivariate Gaussian process; Sensor selection
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