A novel probabilistic sparsity-promoting method for robust near-field (NF) antenna characterization is proposed. It leverages on the measurements-by-design (MebD) paradigm, and it exploits some a priori information on the antenna under test (AUT) to generate an overcomplete representation basis. Accordingly, the problem at hand is reformulated in a compressive sensing (CS) framework as the retrieval of a maximally sparse distribution (with respect to the overcomplete basis) from a reduced set of measured data, and then, it is solved by means of a Bayesian strategy. Representative numerical results are presented to, also comparatively, assess the effectiveness of the proposed approach in reducing the 'burden/cost' of the acquisition process and mitigate (possible) truncation errors when dealing with space-constrained probing systems. © 1963-2012 IEEE.

A Bayesian Compressive Sensing Approach to Robust Near-Field Antenna Characterization

Migliore M. D.;
2022

Abstract

A novel probabilistic sparsity-promoting method for robust near-field (NF) antenna characterization is proposed. It leverages on the measurements-by-design (MebD) paradigm, and it exploits some a priori information on the antenna under test (AUT) to generate an overcomplete representation basis. Accordingly, the problem at hand is reformulated in a compressive sensing (CS) framework as the retrieval of a maximally sparse distribution (with respect to the overcomplete basis) from a reduced set of measured data, and then, it is solved by means of a Bayesian strategy. Representative numerical results are presented to, also comparatively, assess the effectiveness of the proposed approach in reducing the 'burden/cost' of the acquisition process and mitigate (possible) truncation errors when dealing with space-constrained probing systems. © 1963-2012 IEEE.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11580/94042
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