eprintid: 741
rev_number: 35
eprint_status: archive
userid: 7
dir: disk0/00/00/07/41
datestamp: 2017-07-21 12:47:48
lastmod: 2024-04-12 08:54:54
status_changed: 2017-07-21 13:45:32
type: monograph
metadata_visibility: show
creators_name: Anselmi, N.
creators_name: Oliveri, G.
creators_name: Hannan, M.
creators_name: Salucci, M.
creators_name: Massa, A.
title: Free‐Space Microwave Imaging through Alphabet‐Based Bayesian Compressive Sensing
ispublished: pub
subjects: AWC
subjects: MCS
full_text_status: public
monograph_type: technical_report
keywords: Inverse Scattering (IS), Bayesian Compressive Sensing (BCS), Microwave Imaging, First Order Born Approximation
abstract: A key requirement to be satisfied when exploiting Compressive Sensing (CS) methods in inverse scattering (IS) problems is that the unknowns (e.g., the contrast function or the equivalent sources) are sparse with respect to the considered expansion basis. State-of-the-art CS-based microwave imaging techniques typically consider single-resolution pixel-based representations, limiting their domain of applicability to the retrieval of few and isolated pixels within the investigated domain. Within this framework, this work is aimed at extending the range of applicability of CS-based approaches to the retrieval of unknown scatterers having arbitrary shape and dimensions. Since in real applications no a-priori information about the investigation domain is available, the idea is to retrieve a set of "candidate" solutions by executing several CS inversions using different expansion bases (e.g., pixel, Haar wavelets, Meyer wavelets, ...). Following the CS paradigm, the "best" solution can then be identified as the sparsest one, i.e., the solution with the lowest number of non-zero retrieved coefficients. A preliminary numerical validation of the proposed alphabet-based CS microwave imaging technique is given. Some numerical comparisons with competitive state-of-the-art inverse scattering techniques is shown, as well.
date: 2016
publisher: ELEDIA Research Center - University of Trento
referencetext: 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.

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.

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,” J. Opt. Soc. Am. A, vol. 30, no. 6, pp. 1261-1272, 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.

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, March 2017.

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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.

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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.

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.

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.

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.

F. Viani, G. Oliveri, and A. Massa, “Compressive sensing pattern matching techniques for synthesizing planar sparse arrays,” IEEE Trans. Antennas Propag., vol. 61, no. 9, pp. 4577-4587, Sept. 2013.

G. Oliveri, M. Salucci, and A. Massa, “Synthesis of modular contiguously clustered linear arrays through a sparseness-regularized solver,” IEEE Trans. Antennas Propag., vol. 64, no. 10, pp. 4277-4287, Oct. 2016.

P. Rocca, M. A. Hannan, M. Salucci, and A. Massa, “Single-snapshot DoA estimation in array antennas with mutual coupling through a multi-scaling BCS strategy,” IEEE Trans. Antennas Propag., vol. 65, no. 6, pp. 3203-3213, Jun. 2017.

P. Rocca, M. Benedetti, M. Donelli, D. Franceschini, and A. Massa, “Evolutionary optimization as applied to inverse problems,” Inverse Probl., vol. 25, pp. 1-41, Dec. 2009.

P. Rocca, G. Oliveri, and A. Massa, “Differential Evolution as applied to electromagnetics,” IEEE Antennas Propag. Mag., vol. 53, no. 1, pp. 38-49, Feb. 2011.
citation:   Anselmi, N. and Oliveri, G. and Hannan, M. and Salucci, M. and Massa, A.  (2016) Free‐Space Microwave Imaging through Alphabet‐Based Bayesian Compressive Sensing.  Technical Report. ELEDIA Research Center - University of Trento.     
document_url: http://www.eledia.org/students-reports/741/7/Free%E2%80%90Space_Microwave_Imaging_through_Alphabet%E2%80%90Based_Bayesian_Compressive_Sensing.v2.pdf