Abstract
This article presents a novel unsupervised learning framework based on multiscale dynamic mode decomposition for determining the excitation coefficients of antennas using time-domain near-field measurements. The proposed framework integrates temporal multiscale analysis to extract a joint distribution of frequency, damping factors, and spatial modes, enabling precise extraction of excitation frequencies, rising/falling edges, and phases without labeled data. We validate the effectiveness of the proposed approach through two examples involving on-off keying modulation and a phase-shift dipole antenna. It is found that the proposed method performs well in handling nonstationary excitation signals and proves particularly advantageous for calibrating tunable antenna systems. Our work could advance the capabilities of antenna measurements and optimization.
Recommended Citation
Y. Zhang et al., "An Unsupervised Learning Framework for Determining the Excitation Coefficients using Near-Field Antenna Measurements," IEEE Transactions on Electromagnetic Compatibility, vol. 66, no. 6, pp. 1939 - 1946, Institute of Electrical and Electronics Engineers, Jan 2024.
The definitive version is available at https://doi.org/10.1109/TEMC.2024.3427682
Department(s)
Geosciences and Geological and Petroleum Engineering
Second Department
Electrical and Computer Engineering
Keywords and Phrases
Excitation coefficients; multiscale dynamic mode decomposition (MDMD); near-field scanning; phase-shift antenna; unsupervised learning approach
International Standard Serial Number (ISSN)
1558-187X; 0018-9375
Document Type
Article - Journal
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2025 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jan 2024
Included in
Electrical and Computer Engineering Commons, Geology Commons, Geophysics and Seismology Commons
Comments
Innovation and Technology Fund, Grant 4937124