Assessing the Capabilities of a New Linear Inversion Method for Quantitative Microwave Imaging

Donato, Loreto Di; Palmeri, Roberta; Sorbello, G.; Isernia, Tommaso; Crocco, Lorenzo

doi:10.1155/2015/403760

Cited by 16 publications

(19 citation statements)

References 21 publications

(24 reference statements)

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“…While, for the sake of brevity, herein we do not report comparisons, we address the reader to [7,9,24] for a comprehensive performance assessment of these two linearized inversion methods.…”

Section: F Oamdielintt M Dataset: Two Nested Cylindersmentioning

confidence: 99%

The Factorization Method for Virtual Experiments Based Quantitative Inverse Scattering

Crocco¹,

Donato²,

Catapano³

et al. 2016

PIER

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Abstract-The concept of virtual experiments is based on the idea of solving the inverse scattering problem by processing a suitable recombination of the available data, instead of those arising from the measurements. By properly designing such experiments (and without additional measurements), it is possible to enforce some peculiar field's or contrast source's properties, which can be helpful to perform the inversion in a more simple and reliable way. In this paper, we show that the factorization method can be used as a tool to design the virtual experiments. In doing so, we also provide, for the first time, an insight into its physical meaning. As an example, we exploit the virtual experiments designed via FM as the backbone of a linearized inversion approach for quantitative imaging of non-weak targets.

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Section: F Oamdielintt M Dataset: Two Nested Cylindersmentioning

confidence: 99%

The Factorization Method for Virtual Experiments Based Quantitative Inverse Scattering

Crocco¹,

Donato²,

Catapano³

et al. 2016

PIER

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“…On the other hand, once collected, this amount of independent information can be re-organized in a different way by taking advantage from the linearity of the scattering phenomenon. Starting from this simple linear 'transformation' of the scattering data, the concept of VE has been introduced in [6][7][8][9][10][11]. VE represent a re-arrangement of the original experiments (or originally collected information) and they do not require additional measurements.…”

Section: The New Framework Of the Virtual Experimentsmentioning

confidence: 99%

“…A linear approximation for non weak scatterers Following the simple idea above, in [6] a new (target dependent) approximation has been introduced to accurately solve in a non-iterative way inverse scattering problems in a range of cases much broader than the Born approximation [7].…”

Section: Amentioning

confidence: 99%

“…Recently, a new linear approximation, which relies on the emerging framework of the virtual scattering experiments (VE) [6][7][8][9][10][11] and significantly outperforms the usual BA, has been introduced in [6]. As for the above referenced Born Approximations, also this approach can be extended to the case of partially known non-homogeneous scenarios, where it is expected to be able to deal with a range of profiles going beyond the validity of the Distorted Born Approximation (DBA).…”

Section: Introductionmentioning

confidence: 99%

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Microwave imaging via iterated virtual experiments

Bevacqua

Palmeri

Donato³

et al. 2016

2016 10th European Conference on Antennas and Propagation (EuCAP)

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The solution of inverse scattering problems is tackled by means of a novel iterative approach that relies on the emerging virtual scattering experiments' paradigm. In the virtual experiments framework, inverse scattering problems can be solved within a linear framework even for non-weak targets, thanks to the behavior of the fields enforced through proper rearrangements of the scattered field data. Such a concept is herein exploited within an iterative scheme, in which the virtual experiments are recast on the basis of intermediate results and the relevant Green's function updated at each iteration step. Examples dealing with both numerical and experimental data are provided to assess the effectiveness of the proposed method and to show that it overcomes the well-known Distorted Born iterative method, while preserving its flexibility and simplicity. As such, these results demonstrate that the virtual experiment framework can promote the exploitation of microwave imaging in real world applications.

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“…In this paper we introduce, discuss, and validate a linear inversion strategy which takes advantage from the virtual experiments (VE) and a field approximation able to provide a full characterization of nonweak buried objects [ Bevacqua et al , ; Crocco et al , ; Di Donato et al , ] with piecewise constant electromagnetic properties. As a matter of fact, such a class of targets can be considered to be sparse when represented in terms of step functions.…”

Section: Introduction and Motivationsmentioning

confidence: 99%

Exploiting sparsity and field conditioning in subsurface microwave imaging of nonweak buried targets

et al. 2016

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An efficient inverse scattering strategy is proposed to achieve dielectric characterization of buried objects in lossy soils. The approach takes advantage of Virtual Experiments and Compressive Sensing to obtain quantitative reconstructions of nonweak targets which are nonsparse in the pixel representation basis, commonly adopted in microwave imaging. In addition, an original strategy is adopted to overcome the relevant information lack arising when data are gathered under aspect-limited configurations, such as in ground penetrating radar (GPR) surveys. The proposed strategy significantly outperforms the results achievable with the "state of the art" standard approaches since it allows to achieve nearly optimal reconstructions within a linear framework and without increasing the overall computational burden. Numerical examples with simulated data are given to show the feasibility of the proposed strategy.The last drawback can be circumvented, in principle, by adopting imaging recovery approaches based on the emerging framework of compressive sensing (CS) [Baraniuk, 2007;Donoho, 2006]. It offers a powerful framework to obtain satisfactory reconstructions of "sparse images," with the possibility to achieve nearly optimal reconstruction as well as superresolution. However, CS theory has been completely assessed only in the case of linear recovery problems [Baraniuk, 2007;Donoho, 2006] so that in subsurface microwave imaging,

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Assessing the Capabilities of a New Linear Inversion Method for Quantitative Microwave Imaging

Cited by 16 publications

References 21 publications

The Factorization Method for Virtual Experiments Based Quantitative Inverse Scattering

The Factorization Method for Virtual Experiments Based Quantitative Inverse Scattering

Microwave imaging via iterated virtual experiments

Exploiting sparsity and field conditioning in subsurface microwave imaging of nonweak buried targets

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