@techreport{elediasc12638,
            type = {Technical Report},
           title = {Multi-task Bayesian Compressive Sensing Method for Imaging Sparse Metallic Cylinders},
          author = {L. Poli and G. Oliveri and A. Massa},
            year = {2014},
       publisher = {University of Trento},
        abstract = {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.},
        keywords = {Compressive Sensing, Inverse Scattering, Interval Analysis, Evolutionary Optimization},
             url = {http://www.eledia.org/students-reports/638/}
}