Multimodal Sensor Selection for Multiple Spatial Field Reconstruction

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 join...

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Bibliographic Details
Main Authors: Nguyen, Linh, Thiyagarajan, Karthick, Ulapane, Nalika, Kodagoda, Sarath
Format: Conference Proceeding
Language:English
Subjects:
Conferences
Electric potential
Gaussian processes
Industrial electronics
Multimodal sensing
Multimodal sensors
multiple spatial fields
multivariable
multivariate
multivariate Gaussian process
Predictive models
sensor selection
Uncertainty
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Summary: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.
ISSN:2158-2297
DOI:10.1109/ICIEA51954.2021.9516255