@techreport{elediasc12780,
       publisher = {ELEDIA Research Center - University of Trento},
            year = {2018},
            type = {Technical Report},
           title = {Multi?Resolution Imaging through a Novel Bayesian Compressive Sensing Approach},
          author = {N. Anselmi and L. Poli and G. Oliveri and A. Massa},
        keywords = {Born approximation (BA), compressive sensing (CS), inverse scattering (IS), microwave imaging, relevance vector machine (RVM)},
             url = {http://www.eledia.org/students-reports/780/},
        abstract = {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.}
}