TY  - RPRT
AV  - public
N2  - 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.
ID  - elediasc12872
UR  - http://www.eledia.org/students-reports/872/
PB  - ELEDIA Research Center - University of Trento
M1  - technical_report
KW  - Antenna measurements
KW  -  antenna qualification
KW  -  compressive sensing (CS)
KW  -  near-field (NF) pattern estimation
KW  -  near-field to far-field (NF-FF) transformation
KW  -  sparsity retrieval
KW  -  truncation error.
Y1  - 2019///
TI  - A Compressive Sensing-Based Near-Field Antenna Characterization - The Bayesian Approach
A1  - Salucci, Marco
A1  - Anselmi, Nicola
A1  - Massa, Andrea
ER  -