A Bayesian Compressive Sensing Approach to Robust Near-Field Antenna Characterization

TL;DR

This paper introduces a Bayesian compressive sensing method for robust near-field antenna characterization, improving measurement efficiency and reducing errors in space-constrained systems.

Contribution

It presents a novel probabilistic sparsity-promoting approach that leverages a-priori information within a compressive sensing framework for antenna testing.

Findings

Reduces measurement burden and cost.

Mitigates truncation errors in space-constrained setups.

Demonstrates effectiveness through numerical results.

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 over-complete 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 as well as to mitigate (possible) truncation errors when dealing with space-constrained probing systems.