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A Compressive Sensing-Based Near-Field Antenna Characterization - The Bayesian Approach
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.
ELEDIA Research Center - University of Trento
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HTML Summary of #872 A Compressive Sensing-Based Near-Field Antenna Characterization - The Bayesian Approach
A WC Next Generation Wireless Communications
M CS Compressive Sensing