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• W2963037525 abstract "In this paper we consider a network of spatially distributed sensors which collect measurement samples of a spatial field, and aim at estimating in a distributed way (without any central coordinator) the entire field by suitably fusing all network data. We propose a general probabilistic model that can handle both partial knowledge of the physics generating the spatial field as well as a purely data-driven inference. Specifically, we adopt an Empirical Bayes approach in which the spatial field is modeled as a Gaussian Process, whose mean function isdescribed by means of parametrized equations. We characterize the Empirical Bayes estimator when nodes are heterogeneous, i.e., perform a different number of measurements. Moreover, by exploiting the sparsity of both the covariance and the (parametrized) mean function of the Gaussian Process, we are able to design a distributed spatial field estimator. We corroborate the theoretical results with two numerical simulations: a stationary temperature field estimation in which the field is described by a partial differential (heat) equation, and a data driven inference in which the mean is parametrized by a cubic spline." @default.
• W2963037525 created "2019-07-30" @default.
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• W2963037525 date "2018-06-01" @default.
• W2963037525 modified "2023-09-24" @default.
• W2963037525 title "An Empirical Bayes Approach for Distributed Estimation of Spatial Fields" @default.
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• W2963037525 doi "https://doi.org/10.23919/ecc.2018.8550231" @default.
• W2963037525 hasPublicationYear "2018" @default.
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