Time-resolved electromagnetic near-field scanning plays a pivotal role in antenna measurement and unraveling complex electromagnetic interference and compatibility issues. However, the rapid acquisition of high-resolution spatio–temporal data remains challenging due to physical constraints, such as moving the probe position and allowing sufficient time for sampling. This article presents a novel hybrid approach combining kriging for sparse spatial measurement, compressed sensing (cs) for sparse temporal sampling, and dynamic mode decomposition (dmd) for comprehensive analysis of the dual-sparse sampling electromagnetic near-field data. We leverage cs to optimize sparse sampling in the time domain and latin hypercube sampling to guide the probe position and realize sparse measurement in the space domain. By leveraging the inherent sparsity within electromagnetic radiated signals, cs reliably represents time-domain signals while reducing the required time samples. Then, dmd is used to extract meaningful insights from the resulting sparse spatio–temporal data, resulting in the sparse dynamic modes and temporal evolution information. Next, the kriging is employed to infer missing spatial measurements for each sparse dynamic mode. Finally, the entire spatio–temporal signals are reconstructed based on interpolated dynamic modes and temporal evolution information. An example using crossed dipole antennas as the device under test is provided to validate the proposed method. It is found that the proposed kriging-cs-dmd framework effectively reconstructs electromagnetic fields with precision while simultaneously reducing the measurement workload in both the time and space domains. This methodology could be further employed for various applications, such as space–time-modulated electronic devices.


Electrical and Computer Engineering

Publication Status

Early / Open Access

Keywords and Phrases

Compressed sensing (CS); dynamic mode decomposition (DMD); electromagnetic near-field scanning; Electromagnetics; Kriging; Probes; Q measurement; Sparse matrices; Time measurement; Time-domain analysis; time-resolved measurement; Vectors

International Standard Serial Number (ISSN)

1558-187X; 0018-9375

Document Type

Article - Journal

Document Version


File Type





© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.

Publication Date

01 Jan 2024