Tesfaye Gebremariam, Getachew Assefa, Dr. Mesfin Tadesse, 2026. "Broadband Time-Domain Waveform Reconstruction Algorithms for Non-Contact Magnetic Probing", International Journal of Electronics and Communication Engineering Research (IJECER) 1(1): 111-128.

Non-contact magnetic probing is a technique for sensing electromagnetic activity of systems without physical interfacing with the target. The method has also recently attracted much attention in a variety of other applications like integrated circuit diagnosis, electromagnetic interference analysis, fault detection, biomedical sensing, wireless communication monitoring and security assessment of electronic equipment. Non-contact magnetic probes collect data by measuring changes in magnetic fields created by electrical currents and electromagnetic phenomena, rendering them a safe, non-invasive and extremely flexible means of signal acquisition. Nevertheless, the broadband electromagnetic signals are difficult to measure precisely because of sensor bandwidth limitation as well as environmental noise, signal attenuation, setting errors of probes and measurement distortions. Such challenges require the design of sophisticated waveform reconstruction algorithms that can recover time-domain signals from incomplete, noisy and distorted measurements.
Magnetic field measurements alone are only raw data that needs to be reconstructed into accurate time domain waveforms of different underlaying electromagnetic activities, making broadband time-domain waveform reconstruction one of the most important parts of a realistic magnetic sensor. Conventional reconstruction techniques typically involve signal filtering, interpolation, inverse modeling and processing in the frequency domain. Although these methods work reasonably well in ideal testing situations, they will most likely fail with high bandwidth broadband signals low signal-to-noise ratios and complex electromagnetic environment conditions. More recent advancements in signal processing, compressed sensing (CS), machine learning (ML), and deep learning (DL) have opened new opportunities for improved accuracy and efficiency of reconstruction. Such a modern strategy allows for more successful recovery of transient events, high-rate components, and certain nonlinear signal properties that are challenging to catch with previous methods.
This study explores algorithms for reconstructing broadband time-domain waveforms from non-contact magnetic probing systems. This research is in the theory of sensing an electromagnetic field, signal acquisition methods and reconstruction techniques. This survey paper analyzes and compares a wide range of algorithmic techniques adapted from inverse problem formulation, sparse signal recovery, compressed sensing techniques such as adaptive filtering, optimization-based reconstruction and more recently artificial intelligence-driven models. The research includes strategies to reduce noise, enhance signals and minimize measurement uncertainty aimed at improving quality of the reconstructions.

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