Anselmi, N. and Oliveri, G. and Hannan, M. and Salucci, M. and Massa, A. (2015) Dictionary‐Based Bayesian Compressive Sensing for Imaging Arbitrary Scatterers. Technical Report. ELEDIA Research Center - University of Trento.
|
Text
Dictionary‐Based_Bayesian_Compressive_Sensing_for_Imaging_Arbitrary_Scatterers.v2.pdf Download (4MB) | Preview |
Abstract
This work deals with an innovative free-space inverse scattering technique. The developed methodology is based on the exploitation of a Bayesian Compressive Sensing (BCS) solver and a set (or dictionary) of expansion bases. Several BCS-regularized reconstructions are performed using the different bases in the dictionary, and the sparsest solution is selected as the most reliable one. Thanks to such an approach, (i) no a-priori information about the unknown scatterers is required, and (ii) it is possible to extend the range of applicability of standard BCS-based inversion to objects having arbitrary size and shape. In order to verify the effectiveness of the proposed technique, as well as to test its robustness to noise, some illustrative numerical results are shown in the following.
| Item Type: | Monograph (Technical Report) |
|---|---|
| Uncontrolled Keywords: | Inverse Scattering (IS), Bayesian Compressive Sensing (BCS), Microwave Imaging, First Order Born Approximation |
| Subjects: | A Areas > A WC Next Generation Wireless Communications M Methodologies > M CS Compressive Sensing |
| URI: | http://www.eledia.org/students-reports/id/eprint/745 |
Actions (login required)
 |
View Item |
