Abstract
This paper proposes a hybrid method for time-resolved electromagnetic near-field scanning, merging model-based (Gaussian processes regression model, a.k.a. Kriging method) and data-driven (dynamic mode decomposition) techniques. Specifically, Latin hypercube sampling enables spatially sparse measurements, followed by dynamic mode decomposition to analyze resulting sparse spatial-temporal data, extracting frequency information and sparse dynamic modes. The Kriging method is then employed for full-state reconstruction. The proposed approach is evaluated using crossed dipole antennas. Results indicate that, even with a spatial subsampling factor of 130, achieving a fully reconstructed field distribution suitable for engineering applications with frequency information extraction is feasible. This hybrid framework presents a promising avenue to enhance efficiency in electromagnetic near-field measurements, potentially finding applications across diverse electromagnetic measurement scenarios.
Recommended Citation
Y. Zhang et al., "Electromagnetic Near-Field Scanning with a Spatially Sparse Sampling Strategy Utilizing Kriging-Dmd," SPI 2024 - 28th IEEE Workshop on Signal and Power Integrity, Proceedings, Institute of Electrical and Electronics Engineers, Jan 2024.
The definitive version is available at https://doi.org/10.1109/SPI60975.2024.10539222
Department(s)
Geosciences and Geological and Petroleum Engineering
Second Department
Electrical and Computer Engineering
Keywords and Phrases
dynamic mode decomposition; Kriging; spatially sparse sampling strategy; Time-resolved electromagnetic near-field scanning
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
01 Jan 2024
Included in
Electrical and Computer Engineering Commons, Geological Engineering Commons, Petroleum Engineering Commons
Comments
Innovation and Technology Fund, Grant GRF 14210623