eprintid: 682
rev_number: 8
eprint_status: archive
userid: 4
dir: disk0/00/00/06/82
datestamp: 2015-04-13 09:41:47
lastmod: 2015-04-13 09:41:47
status_changed: 2015-04-13 09:41:47
type: thesis
metadata_visibility: show
creators_name: Zanichelli, G.
title: Testing of a Non-Linear Compressive Sensing Solver for 2D Microwave Imaging Applications
ispublished: pub
subjects: DB
subjects: MCS
full_text_status: public
keywords: Compressive Sensing, Inverse Scattering  
abstract: In a non-linear system the outputs are not directly proportional to the inputs. Inverse scattering applications to microwave imaging are intrinsically non-linear because of the relation between the total field and the equivalent currents.
Up to now the theory developed for Compressing Sensing signal recovery assumes that samples are taken using linear measurements. Recently the linear sparse signal model is extended to a general non-linear model, suggesting a greedy algorithm to calculate the sparse coefficients. The aim of the project is to check if the proposed approach is suitable for microwave imaging applications.

date: 2015-01
date_type: completed
institution: University of Trento
department: ELEDIA Research Center @ DISI
thesis_type: bachelor
referencetext: [1]	A. Massa, P. Rocca, and G. Oliveri, "Compressive Sensing in Electromagnetics - A Review," IEEE Antennas and Propagation Magazine, pp. 224-238, vol. 57, no. 1, Feb. 2015.
[2]	G. Oliveri, N. Anselmi, and A. Massa, "Compressive sensing imaging of non-sparse 2D scatterers by a total-variation approach within the Born approximation," IEEE Trans. Antennas Propag., vol. 62, no. 10, pp. 5157-5170, Oct. 2014.
[3]	L. Poli, G. Oliveri, and A. Massa, "Imaging sparse metallic cylinders through a Local Shape Function Bayesian Compressive Sensing approach," Journal of Optical Society of America A, vol. 30, no. 6, pp. 1261-1272, 2013.
[4]	F. Viani, L. Poli, G. Oliveri, F. Robol, and A. Massa, "Sparse scatterers imaging through approximated multitask compressive sensing strategies," Microwave Opt. Technol. Lett., vol. 55, no. 7, pp. 1553-1558, Jul. 2013. 
[5]	L. Poli, G. Oliveri, P. Rocca, and A. Massa, "Bayesian compressive sensing approaches for the reconstruction of two-dimensional sparse scatterers under TE illumination," IEEE Trans. Geosci. Remote Sensing, vol. 51, no. 5, pp. 2920-2936, May 2013.
[6]	L. Poli, G. Oliveri, and A. Massa, "Microwave imaging within the first-order Born approximation by means of the contrast-field Bayesian compressive sensing," IEEE Trans. Antennas Propag., vol. 60, no. 6, pp. 2865-2879, Jun. 2012.
[7]	G. Oliveri, P. Rocca, and A. Massa, "A bayesian compressive sampling-based inversion for imaging sparse scatterers," IEEE Trans. Geosci. Remote Sensing, vol. 49, no. 10, pp. 3993-4006, Oct. 2011. 
[8]	G. Oliveri, L. Poli, P. Rocca, and A. Massa, "Bayesian compressive optical imaging within the Rytov approximation," Optics Letters, vol. 37, no. 10, pp. 1760-1762, 2012.
[9]	L. Poli, G. Oliveri, F. Viani, and A. Massa, "MT-BCS-based microwave imaging approach through minimum-norm current expansion," IEEE Trans. Antennas Propag., vol. 61, no. 9, pp. 4722-4732, Sep. 2013. 

citation:   Zanichelli, G.  (2015) Testing of a Non-Linear Compressive Sensing Solver for 2D Microwave Imaging Applications.  Bachelor thesis, University of Trento.   
document_url: http://www.eledia.org/students-reports/682/1/Abstract.A596.pdf