On the Optimal Field Sensing in Near-Field Characterization

Area of Science:

• Electromagnetics and Optics
• Signal Processing
• Computational Imaging

Background:

• Characterizing electromagnetic sources or scatterers relies on analyzing radiated or scattered field measurements.
• Existing methods often use Singular Value Decomposition (SVD) for linear algebraic problems in near-field/far-field transformations and imaging.
• Discretizing singular functions for field sampling presents a significant challenge in these applications.

Purpose of the Study:

• To develop and validate an approach for defining physically realizable "virtual" field sensors for electromagnetic characterization.
• To investigate the performance of sensor arrays and segmented receivers based on Singular Value Optimization (SVO).
• To compare the effectiveness of generalized Gaussian quadrature with SVO for discretizing reception functionals.

Main Methods:

• Application of Singular Value Optimization (SVO) to define "optimal virtual" field sensors.
• Development of two approaches for physical realization: sensor arrays and receiver segmentation.
• Synthesis of sensor arrays using generalized Gaussian quadrature and elementary sensors based on SVO.

Main Results:

• SVO enables the use of elementary, non-uniformly located field sensors that match the performance of "virtual" sensors.
• Sensor arrays synthesized via generalized Gaussian quadrature demonstrate performance comparable to SVO-based methods.
• The study confirms SVO as an optimal approach for designing efficient field sampling strategies.

Conclusions:

• Singular Value Optimization (SVO) provides an effective and optimal method for designing field sensors in electromagnetic characterization.
• Physically realizable sensor arrays and segmented receivers can achieve the performance of theoretical "virtual" sensors.
• The findings have broad implications for various imaging and measurement techniques across different frequencies.