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

• Published in: IEEE transactions on antennas and propagation Vol. 70; no. 9; pp. 8671 - 8676
• Main Authors: Salucci, M., Anselmi, N., Migliore, M. D., Massa, A.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Antenna measurements; antenna qualification; Antennas; Bayes methods; Bayesian analysis; compressive sensing (CS); Near fields; near-field (NF) pattern estimation; near-field to far-field (NF-FF) transformation; Noise measurement; Probabilistic logic; Robustness (mathematics); Signal to noise ratio; sparsity retrieval; truncation error; Truncation errors; Uncertainty
• ISSN: 0018-926X; 1558-2221
• DOI: 10.1109/TAP.2022.3177528

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.