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On the Optimal Field Sensing in Near-Field Characterization.

Amedeo Capozzoli1, Claudio Curcio1, Angelo Liseno1

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Summary

Characterizing electromagnetic sources is optimized using Singular Value Optimization (SVO). This method defines "optimal virtual" sensors for field measurements, enabling physically realizable sensor arrays.

Keywords:
gaussian quadraturenear-field/far-field transformationsoptimalitysingular value decompositionsingular value optimizationsource/scatterer characterization

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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.