%A N. Anselmi
%A G. Oliveri
%A M. Hannan
%A M. Salucci
%A A. Massa
%I ELEDIA Research Center - University of Trento
%D 2017
%X 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.
%K Inverse Scattering (IS), Bayesian Compressive Sensing (BCS), Microwave Imaging, First Order Born Approximation
%L elediasc12742
%T Innovative Alphabet‐Based Bayesian Compressive Sensing Technique for Imaging Targets with Arbitrary Shape