Electromagnetic emissions were measured from several radio receivers to demonstrate the possibility of detecting and identifying these devices based on their unintended emissions. Radiated fields from the different radio receivers have unique characteristics that can be used to identify these devices by analyzing time-frequency plots of measured radiation. A neural network was also developed for automated device detection.
H. Weng et al., "Neural Network Detection and Identification of Electronic Devices based on their Unintended Emissions," Proceedings of the 2005 International Symposium on Electromagnetic Compatibility (2005, Chicago, IL), vol. 1, pp. 245-249, Institute of Electrical and Electronics Engineers (IEEE), Aug 2005.
The definitive version is available at http://dx.doi.org/10.1109/ISEMC.2005.1513508
2005 International Symposium on Electromagnetic Compatibility, EMC 2005 (2005: Aug. 8-12, Chicago, IL)
Electrical and Computer Engineering
Electromagnetic Compatibility (EMC) Laboratory
Keywords and Phrases
Automated Device Detection; Electromagnetic Emissions; Electromagnetic Waves; Electronic Devices; Neural Nets; Neural Network Detection; Radio Receivers; Time-Frequency Plots; Unintended Emissions; Cross-correlation; Detection; Neural network
International Standard Book Number (ISBN)
International Standard Serial Number (ISSN)
Article - Conference proceedings
© 2005 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.