Masters Theses
Automatic sparse ESM scan using Gaussian process regression /by Jiangshuai Li.
Abstract
"Emission source microscopy (ESM) technique can be utilized for 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 using a motorized scanner. 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 randomly selected 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 1D scans are presented.
This method allowed to reduce the number of samples needed to achieve a certain dynamic range of the image, reducing the overall scanning time and eliminating a need of human intervention into the ESM process. Based on the work of 1D scan and Gaussian Regression Sparse ESM strategy, the second work in this paper extends the application of the ESM with GPR sampling to 2D scenes with multiple sources, including distributed ones.
The automatic GPR ESM method can intelligently and automatically control the scanning process, reducing the number of measurement points with less image quality degradation compared to the random ESM scanning"--Abstract, page iv.
