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.

A Bayesian Compressive Sensing Approach to Robust Near-Field Antenna Characterization / Salucci, M.; Anselmi, N.; Migliore, M. D.; Massa, A.. - In: IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION. - ISSN 0018-926X. - 70:9(2022), pp. 8671-8676. [10.1109/TAP.2022.3177528]

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

Salucci M.;Anselmi N.;Migliore M. D.;Massa A.
2022-01-01

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.
2022
9
Salucci, M.; Anselmi, N.; Migliore, M. D.; Massa, A.
A Bayesian Compressive Sensing Approach to Robust Near-Field Antenna Characterization / Salucci, M.; Anselmi, N.; Migliore, M. D.; Massa, A.. - In: IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION. - ISSN 0018-926X. - 70:9(2022), pp. 8671-8676. [10.1109/TAP.2022.3177528]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/357744
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