Imaging Distributed Sources with Sparse ESM Technique and Gaussian Process Regression

Emission source microscopy (ESM) technique can be utilized for the localization of electromagnetic interference sources in complex and large systems. In this work, a Gaussian process regression (GPR) method is applied in real-time to select sampling points for the sparse ESM imaging. The Gaussian process regression is used to estimate the complex amplitude of the scanned field and its uncertainty allowing to select the most relevant areas for scanning. Compared with the random selection of samples the proposed method allows to reduce the number of samples needed to achieve a certain dynamic range of the image, reducing the overall scanning time. Results for simulated and measured 2D scans for multiple and distributed emission source are presented.