Abstract
Time-resolved electromagnetic near-field scanning is vital for antenna measurement and addressing complex electromagnetic interference and compatibility issues. However, the swift acquisition of high-resolution spatiotemporal data remains challenging due to physical constraints, such as moving the probe position and allowing sufficient time for sampling. This paper introduces a novel hybrid approach that combines Kriging for sparse spatial measurement, compressed sensing (CS) for sparse temporal sampling, and dynamic mode decomposition (DMD) for a comprehensive analysis of dual-sparse sampling electromagnetic near-field data. CS optimizes sparse sampling in the time domain, capitalizing on the inherent sparsity within electromagnetic radiated signals, resulting in reliable representation of time-domain signals and reducing the required time samples. Latin hypercube sampling guides the probe position, facilitating sparse measurement in the space domain. DMD extracts meaningful insights from the resulting sparse spatiotemporal data, producing sparse dynamic modes and temporal evolution information. Subsequently, Kriging is employed to infer missing spatial measurements for each sparse dynamic mode. Finally, the entire spatiotemporal signals are reconstructed based on interpolated dynamic modes and temporal evolution information. Validation of the proposed method is demonstrated with an example using crossed dipole antennas as the device under test. The Kriging-CS-DMD framework effectively reconstructs electromagnetic fields with precision while concurrently reducing the measurement workload in both the time and space domains. This methodology holds promise for various applications, including space-time-modulated electronic devices.
Recommended Citation
Y. Zhang et al., "A Hybrid Algorithm to Dual Sparse Sampling Measurement in Time-Resolved Electromagnetic Near-Field Scanning," IEEE International Symposium on Electromagnetic Compatibility, pp. 71 - 75, Institute of Electrical and Electronics Engineers, Jan 2024.
The definitive version is available at https://doi.org/10.1109/EMCSIPI49824.2024.10705494
Department(s)
Geosciences and Geological and Petroleum Engineering
Second Department
Electrical and Computer Engineering
Keywords and Phrases
compressed sensing; dynamic mode decomposition; Kriging; Time-resolved electromagnetic near-field scanning
International Standard Serial Number (ISSN)
2158-1118; 1077-4076
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, Geology Commons, Geophysics and Seismology Commons
Comments
Innovation and Technology Fund, Grant 14201923