eprintid: 672
rev_number: 8
eprint_status: archive
userid: 4
dir: disk0/00/00/06/72
datestamp: 2015-04-13 08:59:51
lastmod: 2015-04-13 09:14:23
status_changed: 2015-04-13 08:59:51
type: thesis
metadata_visibility: show
creators_name: Rossi, A.
title: Contrast Source inversion techniques based on Bayesian Compressive Sensing from GPR data
ispublished: pub
subjects: DM
subjects: MCS
full_text_status: public
keywords: Compressive Sensing, Inverse Scattering 
abstract: Compressive Sensing (CS) techniques, recently developed in signals processing field, allow to obtain a reliable reconstruction of high resolution signals using a number of measurements extremely lower than the number predicted by the well-known Nyquist-Shannon theorem. CS techniques have been already successfully applied to many practical problems like radar and audio/video compression. More recently, the Compressive Sensing (CS) paradigm has been employed to develop new strategies for imaging sparse scatterers at microwave and optical frequencies exploiting the Born approximation, the Rytov approximation, and the Contrast Source formulation in order to obtain a linear dependency of the unknowns to the collected data, with appreciable results.
This project is aimed to implement a contrast source inversion algorithm for microwave imaging based on the Bayesian Compressive Sampling (BCS) for the reconstruction of sparse buried objects from the GPR aquired data. In particular, the multi-task version of the BCS allows to exploit during the reconstruction process the correlation among multiple problems. In the specific case, this implementation allows to takes into account the correlation between the scattered data provided by multiple frequencies.

date: 2015-04-02
date_type: completed
institution: University of Trento
department: ELEDIA Research Center @ DISI
thesis_type: masters
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. 
[10]	M. Benedetti, D. Lesselier, M. Lambert, and A. Massa, "Multiple shapes reconstruction by means of multi-region level sets," IEEE Trans. Geosci. Remote Sensing, vol. 48, no. 5, pp. 2330-2342, May 2010.
[11]	T. Moriyama, G. Oliveri, M. Salucci, and T. Takenaka, "A multi-scaling forward-backward time-stepping method for microwave imaging," IEICE Electronics Express, vol. 11, no. 16, pp. 1-12, Aug. 2014. 

citation:   Rossi, A.  (2015) Contrast Source inversion techniques based on Bayesian Compressive Sensing from GPR data.  Masters thesis, University of Trento.   
document_url: http://www.eledia.org/students-reports/672/1/Abstract.A463.pdf