eprintid: 736
rev_number: 9
eprint_status: archive
userid: 4
dir: disk0/00/00/07/36
datestamp: 2017-06-22 07:20:17
lastmod: 2018-02-26 15:55:38
status_changed: 2017-06-22 07:20:17
type: monograph
metadata_visibility: show
creators_name: Salucci, M.
creators_name: Poli, L.
creators_name: Anselmi, N.
creators_name: Massa, A.
title: An Innovative Multi-Frequency PSO-Based Method for the Microwave Imaging of  
Buried Objects having Different Conductivities 
ispublished: pub
subjects: AWC
subjects: MEA
full_text_status: public
monograph_type: technical_report
keywords: Ground Penetrating Radar (GPR), Inverse Scattering (IS), Multi-Frequency (MF), Particle Swarm Optimization (PSO), Stochastic Optimization, Wide-band Data, Iterative Multi Scaling Approach (IMSA) 
abstract: In this work, an innovative particle swarm optimization (PSO)-based microwave imaging approach is presented to solve the subsurface inverse scattering problem. The proposed MF-IMSA-PSO method integrates a customized PSO solver within a multi-scaling technique (i.e., the IMSA) in order to limit the ratio between problem unknowns and non-redundant data, mitigating the negative effects of both non-linearity and ill-posedness through the exploitation of progressively acquired information about the solution. Moreover, the inversion is performed by considering  a multi-frequency (MF) solution strategy, by jointly processing several frequency components extracted from the spectrum of the measured data through ground penetrating radar (GPR). Some numerical results are shown  in order to verify the effectiveness of the developed GPR microwave imaging technique when dealing with objects having a conductivity different from that of the hosting (lossy) soil.
date: 2016
publisher: University of Trento
referencetext: [1] 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.
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[4] 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.
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[8] 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.
[9] 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.
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[13] M. Salucci, N. Anselmi, G. Oliveri, P. Calmon, R. Miorelli, C. Reboud, and A. Massa, "Real-time NDT-NDE through an innovative adaptive partial least squares SVR inversion approach," IEEE Trans. Geosci. Remote Sens., vol. 54, no. 11, pp. 6818-6832, Nov. 2016.
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[15] M. Donelli, D. Franceschini, P. Rocca, and A. Massa, "Three-dimensional microwave imaging problems solved through an efficient multiscaling particle swarm optimization," IEEE Trans. Geosci. Remote Sensing, vol. 47, no. 5, pp. 1467-1481, May 2009.
citation:   Salucci, M. and Poli, L. and Anselmi, N. and Massa, A.  (2016) An Innovative Multi-Frequency PSO-Based Method for the Microwave Imaging of Buried Objects having Different Conductivities.  Technical Report. University of Trento.     
document_url: http://www.eledia.org/students-reports/736/1/An%20Innovative%20Multi%E2%80%90Frequency%20PSO%E2%80%90Based%20Method%20for%20the%20Microwave%20Imaging%20of%20Buried%20Objects%20having%20Different%20Conductivities.pdf