eprintid: 911
rev_number: 9
eprint_status: archive
userid: 13
dir: disk0/00/00/09/11
datestamp: 2025-03-28 18:34:09
lastmod: 2025-03-28 18:34:09
status_changed: 2025-03-28 18:34:09
type: monograph
metadata_visibility: show
creators_name: SALUCCI, Marco
creators_name: POLI, Lorenzo
creators_name: ZARDI, Francesco
creators_name: TOSI, Luca
creators_name: LUSA, Samantha
creators_name: MASSA, Andrea
creators_id: marco.salucci@unitn.it
creators_id: lorenzo.poli@unitn.it
creators_id: francesco.zardi@eledia.org
creators_id: luca.tosi@unitn.it
creators_id: samantha.lusa@eledia.org
creators_id: andrea.massa@unitn.it
title: Multi-Resolution Bayesian Compressive Sensing for Sparse Target Inversion
ispublished: pub
subjects: AWC
subjects: MCS
full_text_status: public
monograph_type: technical_report
keywords: microwave imaging (MI), contrast source inversion (CSI), Bayesian compressive sensing (BCS), inverse scattering (IS)
abstract: Non-Born scatterer retrieval is addressed within the contrast source inversion (CSI) framework using a novel multi-step inverse scattering approach. The unknown contrast sources are represented in a multi-resolution (MR) framework and iteratively reconstructed using a Bayesian compressive sensing (BCS) approach, which employs a constrained relevance vector machine solver to promote sparsity. To assess the reliability and robustness of the proposed MR-BCS-CSI method, representative inversions from both synthetic and experimental data are presented. Additionally, comparisons with recent state-of-the-art alternatives are provided.
date: 2025
publisher: ELEDIA Research Center - University of Trento
referencetext: [1] 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. - Special Issue on “Electromagnetic Inverse Problems for Sensing and Imaging,” vol. 59, no. 5, pp. 34-46, Oct. 2017 (DOI: 10.1109/MAP.2017.2731204).

[2] 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 (DOI: 10.1109/MAP.2015.2397092).

[3] A. Massa and F. Texeira, Guest-Editorial: Special Cluster on Compressive Sensing as Applied to Electromagnetics, IEEE Antennas Wireless Propag. Lett., vol. 14, pp. 1022-1026, 2015 (DOI: 10.1109/LAWP.2015.2425011).

[4] M. Salucci, L. Poli, F. Zardi, L. Tosi, S. Lusa, and A. Massa, ‘Contrast source inversion of sparse targets through multi-resolution Bayesian compressive sensing’, Inverse Probl., vol. 40, no. 5, p. 055016, May 2024 (DOI 10.1088/1361-6420/ad3b33).

[5] 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 (DOI: 10.1080/09205071.2023.2188263).

[6] 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 (DOI: 10.1109/TMTT.2019.2899848).

[7] 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 (DOI: 10.3390/jimaging5010019).

[8] 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 (DOI: 10.1080/09205071.2018.1425160).

[9] 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 (DOI: 10.1109/TAP.2018.2826574).

[10] 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 (DOI: 10.1109/TMTT.2016.2645570).

[11] 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 (DOI: 10.1109/TAP.2015.2444423).

[12] 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 (DOI: 10.1109/LAWP.2014.2365523).

[13] L. Poli, G. Oliveri, P.-P. Ding, T. Moriyama, and A. Massa, “Multifrequency Bayesian compressive sensing methods for microwave imaging,” J. Opt. Soc. Am. A, vol. 31, no. 11, pp. 2415-2428, 2014 (DOI: 10.1364/JOSAA.31.002415).

[14] 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 (DOI: 10.1109/TAP.2014.2344673).
citation:   SALUCCI, Marco and POLI, Lorenzo and ZARDI, Francesco and TOSI, Luca and LUSA, Samantha and MASSA, Andrea  (2025) Multi-Resolution Bayesian Compressive Sensing for Sparse Target Inversion.  Technical Report. ELEDIA Research Center - University of Trento.     
document_url: http://www.eledia.org/students-reports/911/1/Multi-Resolution_Bayesian_Compressive_Sensing_for_Sparse_Target_Inversion.pdf