Single Measurement Surface 2-D Near-Field Phase Recovery via Smart Warping Sampling and Factorization of 1-D Polynomials

We propose a new approach to phase retrieval from near-field measurements. By jointly exploiting a smart warping sampling strategy as well as recent advances in the far-field case, the technique enables an optimal solution of the problem by exploiting only one measurement surface and a single probe. This overcomes a common limitation of phaseless near-field techniques, which typically require two distinct data sets. Additionally, the proposed deterministic approach avoids resource-intensive global optimization algorithms, prevents false solutions, and minimizes the number of required measurements. The given theory is supported by several examples, including experimental data and an analysis of the method’s sensitivity to noise.

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Publication Details

Journal
IEEE Transactions on Antennas and Propagation
Published
2025-05-30
DOI
https://doi.org/10.1109/tap.2025.3573178
Citations
1
Primary Topic
Electromagnetic Compatibility and Measurements
Type
article
Field-Weighted Citation Impact
0.73
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article

Single Measurement Surface 2-D Near-Field Phase Recovery via Smart Warping Sampling and Factorization of 1-D Polynomials

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1 citations
IEEE Transactions on Antennas and Propagation
Electromagnetic Compatibility and Measurements
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article

Single Measurement Surface 2-D Near-Field Phase Recovery via Smart Warping Sampling and Factorization of 1-D Polynomials

Tommaso Isernia, Roberta Palmeri, Maria Antonia Maisto, Andrea Francesco Morabito, Raffaele Solimene, Giada M. Battaglia
article en
1 citations

Abstract

We propose a new approach to phase retrieval from near-field measurements. By jointly exploiting a smart warping sampling strategy as well as recent advances in the far-field case, the technique enables an optimal solution of the problem by exploiting only one measurement surface and a single probe. This overcomes a common limitation of phaseless near-field techniques, which typically require two distinct data sets. Additionally, the proposed deterministic approach avoids resource-intensive global optimization algorithms, prevents false solutions, and minimizes the number of required measurements. The given theory is supported by several examples, including experimental data and an analysis of the method’s sensitivity to noise.

IEEE Transactions on Antennas and PropagationVol. 73(9)
University of Campania "Luigi Vanvitelli" (IT), Consorzio Nazionale Interuniversitario per le Telecomunicazioni (IT), University of Reggio Calabria (IT)
Affordable and clean energy
Openalex Percentile: Top 28%
Electromagnetic Compatibility and Measurements
0.73
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