eprintid: 658
rev_number: 8
eprint_status: archive
userid: 4
dir: disk0/00/00/06/58
datestamp: 2014-11-06 09:20:47
lastmod: 2018-02-26 13:56:24
status_changed: 2014-11-06 09:20:47
type: monograph
metadata_visibility: show
creators_name: Poli, L.
creators_name: Oliveri, G.
creators_name: Viani, F.
creators_name: Massa, A.
title: Multi-task Bayesian Compressive Sensing for microwave imaging exploiting the minimum-norm current formulation
ispublished: pub
subjects: MCS
full_text_status: public
monograph_type: technical_report
keywords: Compressive Sensing, Inverse Scattering, Interval Analysis
abstract: In this report, an innovative three-step contrast-source probabilistic technique is proposed for the reconstruction of image 2D-sparse dielectric profiles. Within the formulation of the inverse scattering problem, such an approach combines (i) a SVD-based step to retrieve the minimum-norm currents, (ii) a probabilistic reformulation of the inverse scattering problem in terms of the real and imaginary parts of the sparse contrast currents (iii) a multi-task BCS strategy for properly correlating the unknown variables (real and imaginary parts of contrast source coefficients). An enhanced version of the multi-task implementation that takes into account the correlations real and imaginary parts of contrast source coefficients related to different views is also investigated through a wide set of numerical experiments.
date: 2014
publisher: University of Trento
referencetext: [1] 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.
[2] 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.
[3] 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.
[4] L. Poli, G. Oliveri, P. Rocca, and A. Massa, "Bayesian compressive sensing approaches for the recon- struction of two-dimensional sparse scatterers under TE illumination," IEEE Trans. Geosci. Remote Sensing, vol. 51, no. 5, pp. 2920-2936, May. 2013.
[5] 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.
[6] 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.
[7] 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.
[8] 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, Sept. 2013.
[9] P. Rocca, M. Carlin, L. Manica, and A. Massa, "Microwave imaging within the interval analysis frame- work," Progress in Electromagnetic Research, vol. 143, pp. 675-708, 2013.
[10] P. Rocca, M. Carlin, G. Oliveri, and A. Massa, "Interval analysis as applied to inverse scattering," IEEE International Symposium on Antennas Propag. (APS/URSI 2013), Chicago, Illinois, USA, Jul. 8-14, 2012.
[11] L. Manica, P. Rocca, M. Salucci, M. Carlin, and A. Massa, "Scattering data inversion through interval analysis under Rytov approximation," 7th European Conference on Antennas Propag. (EuCAP 2013), Gothenburg, Sweden, Apr. 8-12, 2013.
[12] P. Rocca, M. Carlin, and A. Massa, "Imaging weak scatterers by means of an innovative inverse scattering technique based on the interval analysis," 6th European Conference on Antennas Propag. (EuCAP 2012), Prague, Czech Republic, Mar. 26-30, 2012.
citation:   Poli, L. and Oliveri, G. and Viani, F. and Massa, A.  (2014) Multi-task Bayesian Compressive Sensing for microwave imaging exploiting the minimum-norm current formulation.  Technical Report. University of Trento.     
document_url: http://www.eledia.org/students-reports/658/1/Multi-task%20Bayesian%20Compressive%20Sensing%20for%20microwave%20imaging%20exploiting%20the%20minimum-norm%20current%20formulation.pdf