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

Autor:	Salucci, Marco, Anselmi, Nicola, Migliore, Marco Donald, Massa, Andrea
Rok vydání:	2021
Předmět:	Computer Science - Information Theory Electrical Engineering and Systems Science - Signal Processing
Druh dokumentu:	Working Paper
DOI:	10.1109/TAP.2022.3177528
Popis:	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. Comment: Submitted to IEEE
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2107.07011 Zobrazit plný text záznamu View this record from Arxiv