Single-sensor source localization using electromagnetic time reversal and deep transfer learning: application to lightning

Mostajabi, Amirhossein (Electromagnetic Compatiblity Laboratory, Swiss Federal Institute of Technology (EPFL) Lausanne, Switzerland) ; Karami, Hamidreza (Electromagnetic Compatibility Laboratory, Swiss Federal Instiute of Technology (EPFL), Lausanne, Switzerland) ; Azadifar, Mohammad (School of Management and Engineering Vaud, HES-SO // University of Applied Sciences Western Switzerland) ; Ghasemi, Alireza (ELCA Informatik AG, Zürich, Switzerland) ; Rubinstein, Marcos (School of Management and Engineering Vaud, HES-SO // University of Applied Sciences Western Switzerland) ; Rachidi, Farhad (Electromagnetic Compatibility Laboratory, Swiss Federal Institute of Technology (EPFL), Lausanne, Switzerland)

Electromagnetic Time Reversal (EMTR) has been used to locate different types of electromagnetic sources. We propose a novel technique based on the combination of EMTR and Machine Learning (ML) for source localization. We show for the first time that ML techniques can be used in conjunction with EMTR to reduce the required number of sensors to only one for the localization of electromagnetic sources in the presence of scatterers. In the EMTR part, we use 2D-FDTD method to generate 2D profiles of the vertical electric field as RGB images. Next, in the ML part, we take advantage of transfer learning techniques by using the pretrained VGG-19 Convolutional Neural Network (CNN) as the feature extractor tool. To the best of our knowledge, this is the first time that the knowledge of pretrained CNNs is applied to simulation-generated images. We demonstrate the skill of the developed methodology in localizing two kinds of electromagnetic sources, namely RF sources with a bandwidth of 0.1–10 MHz and lightning impulses. For the localization of lightning, based on the experimental recordings in the Säntis region, the new approach enables accurate 2D lightning localization using only one sensor, as opposed to current lightning location systems that need at least two sensors to operate.


Article Type:
scientifique
Faculty:
Ingénierie et Architecture
School:
HEIG-VD
Institute:
IICT - Institut des Technologies de l'Information et de la Communication
Date:
2019-11
Pagination:
14 p.
Published in:
Scientific Reports
Numeration (vol. no.):
2019, vol. 9, article no. 17372
DOI:
ISSN:
2045-2322
Appears in Collection:



 Record created 2020-01-07, last modified 2020-01-09

Fulltext:
Download fulltext
PDF

Rate this document:

Rate this document:
1
2
3
 
(Not yet reviewed)