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 Mat´ern
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.