Multivariate versus Univariate Sensor Selection for Spatial Field Estimation

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...

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Bibliographic Details
Main Authors: Nguyen, Linh, Thiyagarajan, Karthick, Ulapane, Nalika, Kodagoda, Sarath
Format: Conference Proceeding
Language:English
Subjects:
Gaussian process
Humidity
Industrial electronics
multimodal sensing
multivariable
multivariate
Predictive models
Sensor selection
Sensors
spatial fields
Temperature measurement
Temperature sensors
Uncertainty
univariate
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Summary: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.
ISSN:2158-2297
DOI:10.1109/ICIEA51954.2021.9516093