A Bayesian Compressive Sensing Approach to Robust Near-Field Antenna Characterization
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 over-complete 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 as well as to mitigate (possible) truncation errors when dealing with space-constrained probing systems.
Cite
@article{arxiv.2107.07011,
title = {A Bayesian Compressive Sensing Approach to Robust Near-Field Antenna Characterization},
author = {Marco Salucci and Nicola Anselmi and Marco Donald Migliore and Andrea Massa},
journal= {arXiv preprint arXiv:2107.07011},
year = {2022}
}
Comments
Submitted to IEEE