@techreport{elediasc12742,
          author = {N. Anselmi and G. Oliveri and M. Hannan and M. Salucci and A. Massa},
           title = {Innovative Alphabet?Based Bayesian Compressive Sensing Technique for Imaging Targets with Arbitrary Shape},
            type = {Technical Report},
            year = {2017},
       publisher = {ELEDIA Research Center - University of Trento},
        keywords = {Inverse Scattering (IS), Bayesian Compressive Sensing (BCS), Microwave Imaging, First Order Born Approximation},
             url = {http://www.eledia.org/students-reports/742/},
        abstract = {In this work an innovative two-dimensional (2D) microwave imaging technique exploiting Bayesian Compressive Sensing (BCS) and a wavelet-based alphabet for representing the problem unknowns is dealt with. The proposed approach is based on the generalization of the sparsity concept, extending the range of applicability of BCS-based inverse scattering (IS) techniques to objects with arbitrary shape and dimensions. A set of BCS reconstructions is performed considering different expansion bases in the alphabet, without the need for a-priori knowledge about the unknown scatterers. Then, the best reconstruction is recognized as that minimizing the number of non-null retrieved coefficients (i.e., the sparsest one). In order to verify the effectiveness of the proposed imaging technique, a set of representative numerical benchmarks is presented. Some comparisons with state-of-the-art IS techniques are presented, as well.}
}