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.

Department(s)

Geosciences and Geological and Petroleum Engineering

Second Department

Electrical and Computer Engineering

Comments

Innovation and Technology Fund, Grant 4937124

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

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