eprintid: 870
rev_number: 9
eprint_status: archive
userid: 11
dir: disk0/00/00/08/70
datestamp: 2024-02-15 14:42:23
lastmod: 2024-02-19 10:52:54
status_changed: 2024-02-15 14:42:23
type: monograph
metadata_visibility: show
creators_name: Salucci, Marco
creators_name: Anselmi, Nicola
creators_name: Massa, Andrea
title: Near-Field Antenna Characterization Through a Compressive Sensing Based Approach
ispublished: pub
subjects: AWC
subjects: MCS
full_text_status: public
monograph_type: technical_report
keywords: Antenna measurements, antenna qualification, compres15 sive sensing (CS), near-field (NF) pattern estimation, near-field to far-field (NF-FF) transformation, sparsity retrieval, truncation error.
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.
date: 2019
publisher: ELEDIA Research Center - University of Trento
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citation:   Salucci, Marco and Anselmi, Nicola and Massa, Andrea  (2019) Near-Field Antenna Characterization Through a Compressive Sensing Based Approach.  Technical Report. ELEDIA Research Center - University of Trento.     
document_url: http://www.eledia.org/students-reports/870/1/Near-Field_Antenna_Characterization_Through_a_Compressive_Sensing_Based_Approach.pdf