  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.
