eprintid: 740
rev_number: 9
eprint_status: archive
userid: 7
dir: disk0/00/00/07/40
datestamp: 2017-07-14 11:17:21
lastmod: 2018-02-26 13:54:03
status_changed: 2017-07-14 11:17:21
type: monograph
metadata_visibility: show
creators_name: Salucci, M.
creators_name: Poli, L.
creators_name: Anselmi, N.
creators_name: Massa, A.
title: An Evolutionary Optimization Method for Solving the Multi-Frequency GPR Subsurface Problem
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, the two-dimensional subsurface imaging problem is solved by processing wide-band ground penetrating radar (GPR) data. Towards this end, an innovative microwave inverse scattering technique is presented. The developed methodology is based on a particle swarm optimization (PSO) solver to minimize the arising multi-frequency (MF) cost function measuring the mismatch between measured and retrieved data. Moreover, an iterative multi-resolution strategy is exploited in order to progressively and adaptively refine the resolution of the retrieved images only within the regions of interest in which the presence of a buried object has been detected. Some numerical results are shown, in order to assess the effectiveness of the developed MF-IMSA-PSO strategy in reconstructing dielectric objects buried at different depths. Some experiments are also shown to verify the robustness of the proposed method when the background permittivity is not constant but smoothly varying with the distance from the interface.
date: 2017
publisher: ELEDIA Research Center - University of Trento
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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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T. Moriyama, M. Salucci, M. Tanaka, and T. Takenaka, “Image reconstruction from total electric field data with no information on the incident field,” J. Electromagnet. Wave., vol. 30, no. 9, pp. 1162-1170, 2016.

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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.

L. Poli, G. Oliveri, and A. Massa, “Imaging sparse metallic cylinders through a local shape function bayesian compressing sensing approach,” J. Opt. Soc. Am. A, vol. 30, no. 6, pp. 1261-1272, Jun. 2013.

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.  (2017) An Evolutionary Optimization Method for Solving the Multi-Frequency GPR Subsurface Problem.  Technical Report. ELEDIA Research Center - University of Trento.     
document_url: http://www.eledia.org/students-reports/740/1/An%20Evolutionary%20Optimization%20Method%20for%20Solving%20the%20Multi-Frequency%20GPR%20Subsurface%20Problem.pdf