eprintid: 732
rev_number: 13
eprint_status: archive
userid: 4
dir: disk0/00/00/07/32
datestamp: 2017-05-25 07:22:11
lastmod: 2025-09-01 17:27:16
status_changed: 2017-05-25 07:22:11
type: monograph
metadata_visibility: show
creators_name: Salucci, M.
creators_name: Poli, L.
creators_name: Anselmi, N.
creators_name: Massa, A.
title: Multi-Frequency Multi-Resolution Stochastic Optimization for GPR Microwave Imaging
ispublished: pub
subjects: AWC
subjects: MEA
full_text_status: public
monograph_type: technical_report
keywords: Ground Penetrating Radar (GPR), Inverse Scattering (IS), Frequency-Hopping (FH), Multi-Frequency (MF), Particle Swarm Optimization (PSO), Stochastic Optimization, Wide-band Data, Iterative Multi-Scaling Approach (IMSA)
abstract: In this work, the retrieval of the dielectric characteristics of unknown objects buried in a lossy half-space is dealt with. An innovative multi-resolution multi-frequency (MF) stochastic microwave imaging technique is proposed to solve the buried inverse scattering problem by processing wide-band ground penetrating radar (GPR) data. The proposed MF-IMSA-PSO method exploits a particle swarm optimization (PSO)-based algorithm to find the global optimum of the MF cost function measuring the mismatch between available and retrieved data at a fixed set of frequencies. Such a stochastic solver is nested within the iterative multi-scaling approach (IMSA)  in order to reduce the ratio between problem unknowns and informative data, as well as to adaptively enforce increasing resolutions only within the regions of interest in which the scatterers have been detected. A preliminary numerical validation is shown, in order to assess the robustness of the developed approach with respect to noise, as well as to compare its performance to state-of-the-art competitive approaches.
date: 2017
publisher: University of Trento
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citation:   Salucci, M. and Poli, L. and Anselmi, N. and Massa, A.  (2017) Multi-Frequency Multi-Resolution Stochastic Optimization for GPR Microwave Imaging.  Technical Report. University of Trento.     
document_url: http://www.eledia.org/students-reports/732/1/Multi%E2%80%90Frequency_Multi%E2%80%90Resolution_Stochastic_Optimization_for_GPR_Microwave_Imaging.v2.pdf