EUROPT 2025
Abstract Submission

474. Phase recovery from masked phaseless antenna measurements

Invited abstract in session MD-12: Applications of optimisation under uncertainty, stream Applications: AI, uncertainty management and sustainability.

Monday, 16:30-18:30
Room: B100/8009

Authors (first author is the speaker)

1. Sakirudeen Abdulsalaam
Mathematics, LMU Munich; Munich Centre of Machine Learning
2. Adrien Guth
RWTH-Aachen University
3. Holger Rauhut
LMU Munich; Munich Centre of Machine Learning
4. Dirk Heberling
RWTH-Aachen University; Fraunhofer Institute for High Frequency Physics and Radar Techniques

Abstract

The radiation characteristics of an antenna under test (AUT) is one of the most important antenna properties. Spherical Near Field (SNF) measurements are known to be the most accurate characterization method. Despite its accuracy, SNF measurements pose several challenges, including the need for a significant number of samples and complicated mathematical transformation to derive the AUT's far-field (FF) radiation pattern from the complex near-field (NF) measurements. Furthermore, the phase acquisition becomes more challenging at higher frequencies. Therefore, research into AUT's characterization with measurements and transformation techniques based on amplitude information only has gained traction. The key challenge in this case is to compute coefficients describing the AUT's radiation behaviour from amplitude NF measurements. PhaseLift, a convex programming technique, has been shown in the literature to be one of the successful methods for phase recovery from discrete Fourier Transform (DFT) measurements with random masks. In this work, we extend this technique to antenna measurements. We prove stable recovery by showing that the sampling operator is well-conditioned. Numerical experiments with noiseless data corroborate the theory.

Keywords

Status: accepted

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