Imaging Distributed Sources with Sparse ESM Technique and Gaussian Process Regression

Abstract

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.

Meeting Name

2021 IEEE International Joint Electromagnetic Compatibility Signal and Power Integrity and EMC Europe Symposium, EMC/SI/PI/EMC Europe 2021 (2021: Jul. 26-Aug. 13, Raleigh, NC)

Department(s)

Electrical and Computer Engineering

Research Center/Lab(s)

Electromagnetic Compatibility (EMC) Laboratory

Comments

This work was supported in part by the National Science Foundation (NSF) under Grant IIP-1916535.

Keywords and Phrases

2D Scan; Emission Sources; ESM; Gaussian Regression; Location; Radiation Strength; Smart Scanner

International Standard Book Number (ISBN)

978-166544888-8

Document Type

Article - Conference proceedings

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2021 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

Publication Date

13 Aug 2021

Link to Full Text

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