relation: http://www.eledia.org/students-reports/638/
title: Multi-task Bayesian Compressive Sensing Method for Imaging Sparse Metallic Cylinders
creator: Poli, L.
creator: Oliveri, G.
creator: Massa, A.
subject: M CS Compressive Sensing
subject: M EA Evolutionary Algorithms
description: In this report, an innovative method for the localization of multiple sparse metallic targets is proposed. Starting from the local shape function formulation of the inverse scattering problem and exploiting the multitask Bayesian compressive sensing paradigm, a two-step approach is applied where, after a first estimation of the LSF scattering amplitudes, the reconstruction of the metallic objects is yielded through a thresholding and voting step. The calibration of the BCS parameters together with some preliminary results dealing with small scatterers reported.
publisher: University of Trento
date: 2014
type: Monograph
type: NonPeerReviewed
format: text
language: en
identifier: http://www.eledia.org/students-reports/638/1/Multi-task%20Bayesian%20Compressive%20Sensing%20Method%20for%20Imaging%20Sparse%20Metallic%20Cylinders.pdf
identifier:   Poli, L. and Oliveri, G. and Massa, A.  (2014) Multi-task Bayesian Compressive Sensing Method for Imaging Sparse Metallic Cylinders.  Technical Report. University of Trento.