Salucci, Marco and Anselmi, Nicola and Massa, Andrea (2019) A Compressive Sensing-Based Near-Field Antenna Characterization - The Bayesian Approach. Technical Report. ELEDIA Research Center - University of Trento.
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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.
| Item Type: | Monograph (Technical Report) |
|---|---|
| Uncontrolled 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. |
| Subjects: | A Areas > A WC Next Generation Wireless Communications M Methodologies > M CS Compressive Sensing |
| Divisions: | University of Trento > Faculty of Telecommunications, Electronics Engineering > Department of Information Engineering and Computer Science > ELEDIA Research Center |
| URI: | http://www.eledia.org/students-reports/id/eprint/872 |
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