Physics-Informed Signal Processing for Accelerating Electromagnetic Sensors

Accurately detecting and processing electromagnetic (EM) signals from moving sensors is challenging due to distortions caused by acceleration and nonuniform time sampling. These effects introduce frequency modulation, nonlinear phase shifts, and directional changes that degrade signal quality and make reconstruction difficult. Traditional signal processing methods often fail to account for these complexities, especially in dynamic environments or layered media. This creates a need for more advanced techniques that can reliably recover accurate signals from accelerating EM sensors.
 

Researchers at Arizona State University have developed a physics-informed method to reconstruct EM signals affected by sensor motion. The approach applies transformations to handle nonuniform time sampling and uses randomized algorithms grounded in Maxwell’s equations to model signal behavior. By accounting for nonlinear phase changes and directionality effects, the method enables precise reconstruction of sparse EM signals in both homogeneous and stratified media, enhancing signal clarity and reliability in dynamic environments. This results in significantly improved signal recovery accuracy for moving sensor systems.

 

This technology presents an advanced method to accurately reconstruct time series signals from accelerating electromagnetic sensors by incorporating physics-based models and nonuniform sampling techniques.

 

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