eprintid: 781
rev_number: 12
eprint_status: archive
userid: 7
dir: disk0/00/00/07/81
datestamp: 2018-08-24 07:09:16
lastmod: 2024-07-09 15:08:55
status_changed: 2018-08-24 07:09:16
type: monograph
metadata_visibility: show
creators_name: Anselmi, N.
creators_name: Poli, L.
creators_name: Oliveri, G.
creators_name: Massa, A.
title: Multi‐Scaling Bayesian Compressive Sensing Imaging of Dielectric Objects
ispublished: pub
subjects: AWC
subjects: MAT
subjects: MCS
full_text_status: public
monograph_type: technical_report
keywords: Born approximation (BA), compressive sensing (CS), inverse scattering (IS), microwave imaging, relevance vector machine (RVM)
abstract: In this work, a new Bayesian compressive sensing (BCS)-based imaging technique is proposed to exploit additional information besides that on the target sparsity. More precisely, an innovative iterative multi-scaling (IMSA)-BCS scheme is proposed to combine the a-priori knowledge on the class of scatterers and the progressively acquired information on the location and the size of the unknown object. Accordingly the 2D transverse magnetic (TM) inverse scattering problem is solved by means of an innovative IMSA-based information-driven relevance vector machine (RVM) solver. Some numerical results are shown to verify the effectiveness of the proposed imaging technique.
date: 2018
publisher: ELEDIA Research Center - University of Trento
referencetext: M. Salucci, G. Oliveri, and A. Massa, “GPR prospecting through an inverse scattering frequency-hopping multi-focusing approach,” IEEE Trans. Geosci. Remote Sens., vol. 53, no. 12, pp. 6573-6592, Dec. 2015.

M. Salucci, L. Poli, N. Anselmi, and A. Massa, "Multifrequency Particle Swarm Optimization for enhanced multiresolution GPR microwave imaging," IEEE Trans. Geosci. Remote Sens., vol. 55, no. 3, pp. 1305-1317, Mar. 2017.

M. Salucci, L. Poli, and A. Massa, "Advanced multi-frequency GPR data processing for non-linear deterministic imaging," Signal Processing - Special Issue on 'Advanced Ground-Penetrating Radar Signal-Processing Techniques,' vol. 132, pp. 306-318, Mar. 2017.

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.

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.

A. Massa, P. Rocca, and G. Oliveri, "Compressive sensing in electromagnetics - A review," IEEE Antennas Propag. Mag., pp. 224-238, vol. 57, no. 1, Feb. 2015.

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.

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.

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.

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.

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. 

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.

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 Sens., vol. 51, no. 5, pp. 2920-2936, May 2013.

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.

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.

G. Oliveri, P. Rocca, and A. Massa, "A Bayesian compressive sampling-based inversion for imaging sparse scatterers," IEEE Trans. Geosci. Remote Sens., vol. 49, no. 10, pp. 3993-4006, Oct. 2011.

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.

M. Salucci, A. Gelmini, L. Poli, G. Oliveri, and A. Massa, “Progressive compressive sensing for exploiting frequency-diversity in GPR imaging,” Journal of Electromagnetic Waves and Applications, vol. 32, no. 9, pp. 1164- 1193, 2018.
citation:   Anselmi, N. and Poli, L. and Oliveri, G. and Massa, A.  (2018) Multi‐Scaling Bayesian Compressive Sensing Imaging of Dielectric Objects.  Technical Report. ELEDIA Research Center - University of Trento.     
document_url: http://www.eledia.org/students-reports/781/1/Multi-Scaling_Bayesian_Compressive_Sensing_Imaging_of_Dielectric_Objects.v2.pdf