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Gaussian Process-based Spatial Reconstruction of Electromagnetic fields

These days we live in a world with a permanent electromagnetic field. This raises many questions about our health and the deployment of new equipment. The problem is that these fields remain difficult to visualize easily, which only some experts can understand. To tackle this problem, we propose to spatially estimate the level of the field based on a few observations at all positions of the considered space. This work presents an algorithm for spatial reconstruction of electromagnetic fields using the Gaussian Process. We consider a spatial, physical phenomenon observed by a sensor network. A Gaussian Process regression model with selected mean and covariance function is implemented to develop a 9 sensors-based estimation algorithm. A Bayesian inference approach is used to perform the model selection of the covariance function and to learn the hyperparameters from our data set. We present the prediction performance of the proposed model and compare it with the case where the mean is zero. The results show that the proposed Gaussian Process-based prediction model reconstructs the EM fields in all positions only using 9 sensors.

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