eprintid: 896
rev_number: 9
eprint_status: archive
userid: 14
dir: disk0/00/00/08/96
datestamp: 2024-10-09 13:01:47
lastmod: 2024-10-09 13:01:47
status_changed: 2024-10-09 13:01:47
type: monograph
metadata_visibility: show
creators_name: OLIVERI, Giacomo
creators_name: ANSELMI, Nicola
creators_name: SALUCCI, Marco
creators_name: POLI, Lorenzo
creators_name: MASSA, Andrea
creators_id: giacomo.oliveri@unitn.it
creators_id: nicola.anselmi.1@unitn.it
creators_id: marco.salucci@unitn.it
creators_id: lorenzo.poli@unitn.it
creators_id: andrea.massa@unitn.it
title: Collecting Scattering Data for Microwave Imaging by means of the Compressive Sensing Technique
ispublished: pub
subjects: AWC
subjects: MCS
full_text_status: public
monograph_type: technical_report
keywords: Compressive Sensing, Inverse Scattering, Inverse Scattering (Free-space)
abstract: This study explores the design of data acquisition systems for microwave imaging, focusing on minimizing the Restricted Isometry Constant (RIC) bound of the observation matrix, which models the acquisition system.
Initially, the collection of scattering data is framed within the context of Compressive Sampling (CS), an approach that reduces the number of required samples by exploiting signal sparsity.
A novel incoherence-based RIC bound is introduced, incorporating the physical attributes of the microwave imaging system. Several numerical experiments are conducted to assess the method’s performance in data acquisition and its influence on CS-based inversion, compared to conventional Nyquist–Shannon sampling techniques.
date: 2024-09-19
publisher: ELEDIA Research Center - University of Trento
referencetext: [1] M. Salucci, L. Poli, and G. Oliveri, “Full-vectorial 3D microwave imaging of sparse scatterers through a multi-task Bayesian compressive sensing approach,” Journal of Imaging, vol. 5, no. 1, pp. 1-24, Jan. 2019.
[2] M. Salucci, A. Gelmini, L. Poli, G. Oliveri, and A. Massa, “Progressive compressive sensing for exploiting frequency-diversity in GPR imaging,” J. Electromagn. Waves Appl. J, vol. 32, no. 9, pp. 1164-1193, 2018.
[3] G. Oliveri, P.-P. Ding, and L. Poli “3D crack detection in anisotropic layered media through a sparseness-regularized solver,”IEEE Antennas Wireless Propag. Lett., vol. 14, pp. 1031-1034, 2015.
[4] Y. Zhong, F. Zardi, M. Salucci, G. Oliveri, and A. Massa, “Multiscaling differential contraction integral method for inverse scattering problems with inhomogeneous media,” IEEE Trans. Microw. Theory Tech., vol. 71, no. 9, pp. 4064-4079, Sep. 2023.
[5] X. Ye, F. Zardi, M. Salucci, and A. Massa, “Multiresolution subspace-based optimization method for the retrieval of 2-D perfect electric conductors,” IEEE Trans. Microw. Theory Tech., vol. 71, no. 4, pp. 1732-1744, Apr. 2023.
[6] G. Oliveri, N. Anselmi, M. Salucci, L. Poli, and A. Massa, “Compressive sampling-based scattering data acquisition in microwave imaging,” J. Electromagn. Waves Appl. J, vol. 37, no. 5, 693-729, Mar. 2023.
[7] M. Salucci, L. Poli, P. Rocca, and A. Massa, “Learned global optimization for inverse scattering problems - Matching global search with computational efficiency,” IEEE Trans. Antennas Propag., vol. 70, no. 8, pp. 6240-6255, Aug. 2022.
[8] Y. Zhong, M. Salucci, K. Xu, A. Polo, and A. Massa, “A multi-resolution contraction integral equation method for solving highly non-linear inverse scattering problems,” IEEE Trans. Microw. Theory Tech., vol. 68, no. 4, pp. 1234-1247, Apr. 2020.
[9] G. Oliveri, L. Poli, N. Anselmi, M. Salucci, and A. Massa, “Compressive sensing-based Born iterative method for tomographic imaging,” IEEE Tran. Microw. Theory Techn., vol. 67, no. 5, pp. 1753-1765, May 2019.
[10] N. Anselmi, L. Poli, G. Oliveri, and A. Massa, “Iterative multi-resolution bayesian CS for microwave imaging,” IEEE Trans. Antennas Propag., vol. 66, no. 7, pp. 3665-3677, Jul. 2018.
[11] G. Oliveri, M. Salucci, and N. Anselmi, “Tomographic imaging of sparse low-contrast targets in harsh environments through matrix completion,” IEEE Trans. Microw. Theory Tech., vol. 66, no. 6, pp. 2714-2730, Jun. 2018.
[12] G. Oliveri, M. Salucci, N. Anselmi, and A. Massa, “Compressive sensing as applied to inverse problems for imaging: theory, applications, current trends, and open challenges,” IEEE Antennas Propag. Mag., vol. 59, no. 5, pp. 34-46, Oct. 2017.
[13] N. Anselmi, G. Oliveri, M. A. Hannan, M. Salucci, and A. Massa, “Color compressive sensing imaging of arbitrary-shaped scatterers,” IEEE Trans. Microw. Theory Techn., vol. 65, no. 6, pp. 1986-1999, Jun. 2017.
[14] T. Moriyama, M. Salucci, M. Tanaka, and T. Takenaka, “Image reconstruction from total electric field data with no information on the incident field,” J. Electromagn. Waves Appl. J, vol. 30, no. 9, pp. 1162-1170, 2016.
[15] N. Anselmi, G. Oliveri, M. Salucci, and A. Massa, “Wavelet-based compressive imaging of sparse targets,” IEEE Trans. Antennas Propag., vol. 63, no. 11, pp. 4889-4900, Nov. 2015.
[16] X. Ye, L. Poli, G. Oliveri, Y. Zhong, K. Agarwal, A. Massa, and X. Chen,“Multi-resolution subspace-based optimization method for solving three-dimensional inverse scattering problems,” J. Opt. Soc. America A, vol. 32, no. 11, pp. 2218-2226, Nov. 2015.
[17] 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.
[18] 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.
[19] L. Poli, G. Oliveri, P.-P. Ding, T. Moriyama, and A. Massa, “Multifrequency Bayesian compressive sensing methods for microwave imaging,” J. Opt. Soc. America A, vol. 31, no. 11, pp. 2415-2428, 2014.
[20] L. Poli, G. Oliveri, and A. Massa, “Imaging sparse metallic cylinders through a Local Shape Function Bayesian Compressive Sensing approach,” J. Opt. Soc. America A, vol. 30, no. 6, pp. 1261-1272, 2013.
[21] 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.
[22] 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.
[23] 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.
[24] 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.
[25] G. Oliveri, A. Randazzo, M. Pastorino, and A. Massa,“Electromagnetic imaging within the contrast-source formulation by means of the multiscaling inexact Newton method,” J. Opt. Soc. America A, vol. 29, no. 6, pp. 945-958, 2012.
[26] 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.
[27] 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.
[28] G. Oliveri, Y. Zhong, X. Chen, and A. Massa, “Multi-resolution subspace-based optimization method for inverse scattering,” J. Opt. Soc. America A, vol. 28, no. 10, pp. 2057-2069, Oct. 2011.
[29] A. Randazzo, G. Oliveri, A. Massa, and M. Pastorino, “Electromagnetic inversion with the multiscaling inexact-Newton method - Experimental validation,” Microwave Opt. Technol. Lett., vol. 53, no. 12, pp. 2834-2838, Dec. 2011.
[30] 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.
[31] M. Benedetti, D. Lesselier, M. Lambert, and A. Massa, “A multi-resolution technique based on shape optimization for the reconstruction of homogeneous dielectric objects,” Inverse Problems, vol. 25, no. 1, pp. 1-26, Jan. 2009.
citation:   OLIVERI, Giacomo and ANSELMI, Nicola and SALUCCI, Marco and POLI, Lorenzo and MASSA, Andrea  (2024) Collecting Scattering Data for Microwave Imaging by means of the Compressive Sensing Technique.  Technical Report. ELEDIA Research Center - University of Trento.     
document_url: http://www.eledia.org/students-reports/896/1/Collecting_Scattering_Data_for_Microwave_Imaging_by_means_of_the_Compressive_Sensing_Technique.pdf