Radiation field estimation using a Gaussian mixture

The problem of estimating the spatial distribution of radiation using measurements from a collection of spatially distributed sensors is considered. A parametric approach is adopted in which the field is modelled by a weighted sum of Gaussians, i.e., a Gaussian mixture. This is a valid approach for a large class of fields, e.g., absolutely integrable fields. Two Bayesian estimators based on progressive correction are proposed to estimate the mixture parameters. The first performs progressive correction using a Gaussian approximation while the second uses a Monte Carlo approximation. It is demonstrated that the Gaussian approximation is capable of accurate estimation using both simulated and real data.