relation: http://www.eledia.org/students-reports/780/
title: Multi‐Resolution Imaging through a Novel Bayesian Compressive Sensing Approach
creator: Anselmi, N.
creator: Poli, L.
creator: Oliveri, G.
creator: Massa, A.
subject: A WC Next Generation Wireless Communications
subject: M AT Analytic Techniques
subject: M CS Compressive Sensing
description: In this work, a multi-resolution strategy is proposed for improving the reconstruction capabilities  of standard Bayesian compressive sensing (BCS) when dealing with the imaging of sparse targets. Towards this end, a customized relevance vector machine (RVM) solver is derived and implemented in order to exploit the progressively acquired information about the scatterer shape and location within the imaged domain.  Some numerical results are shown to validate the effectiveness of the proposed imaging technique.
publisher: ELEDIA Research Center - University of Trento
date: 2018
type: Monograph
type: NonPeerReviewed
format: text
language: en
identifier: http://www.eledia.org/students-reports/780/1/Multi%E2%80%90Resolution_Imaging_through_a_Novel_Bayesian_Compressive_Sensing_Approach.v2.pdf
identifier:   Anselmi, N. and Poli, L. and Oliveri, G. and Massa, A.  (2018) Multi‐Resolution Imaging through a Novel Bayesian Compressive Sensing Approach.  Technical Report. ELEDIA Research Center - University of Trento.