@techreport{elediasc12872,
            year = {2019},
       publisher = {ELEDIA Research Center - University of Trento},
           title = {A Compressive Sensing-Based Near-Field Antenna Characterization - The Bayesian Approach},
          author = {Marco Salucci and Nicola Anselmi and Andrea Massa},
            type = {Technical Report},
        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.},
        keywords = {Antenna measurements, antenna qualification, compressive sensing (CS), near-field (NF) pattern estimation, near-field to far-field (NF-FF) transformation, sparsity retrieval, truncation error.},
             url = {http://www.eledia.org/students-reports/872/}
}