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Multigrid-Based Inversion for Volumetric Radar Imaging With Asteroid Interior Reconstruction as a Potential Application

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Multigrid-Based Inversion for Volumetric Radar Imaging With Asteroid Interior Reconstruction as a Potential Application. / Takala, M.; Us, D.; Pursiainen, S.

julkaisussa: IEEE Transactions on Computational Imaging, Vuosikerta 4, Nro 2, 01.06.2018, s. 228-240.

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Takala, M. ; Us, D. ; Pursiainen, S. / Multigrid-Based Inversion for Volumetric Radar Imaging With Asteroid Interior Reconstruction as a Potential Application. Julkaisussa: IEEE Transactions on Computational Imaging. 2018 ; Vuosikerta 4, Nro 2. Sivut 228-240.

Bibtex - Lataa

@article{5952f9240d2645aeb56f042b682260e2,
title = "Multigrid-Based Inversion for Volumetric Radar Imaging With Asteroid Interior Reconstruction as a Potential Application",
abstract = "This study concentrates on advancing mathematical and computational methodology for radar tomography imaging in which the unknown volumetric velocity distribution of a wave within a bounded domain is to be reconstructed. Our goal is to enable effective simulation and inversion of a large amount of full-wave data within a realistic 2-D or 3-D geometry. For propagating and inverting the wave, we present a rigorous multigrid-based forward approach that utilizes the finite-difference time-domain method and a nested finite element grid structure. We also introduce and validate a multigrid-based inversion algorithm that allows regularization of the unknown distribution through a coarse-to-fine inversion scheme. Using this approach, sparse signals can be effectively inverted, as the coarse fluctuations are reconstructed before the finer ones. Furthermore, the number of nonzero entries in the system matrix can be compressed and, thus, the inversion procedure can be speeded up. As the test scenario, we investigate satellite-based asteroid interior reconstruction. We use both full-wave and projected wave data and estimate the accuracy of the inversion under different error sources: noise and positioning inaccuracies. The results suggest that the present inversion technique allows recovering the interior with a single satellite recording backscattering data. Robust results can be achieved, when the peak-to-peak signal-to-noise ratio is above 10 dB. Furthermore, the robustness for the deep interior part can be enhanced if two satellites can be utilized in the measurements.",
keywords = "finite difference time-domain analysis, geometry, inverse problems, radar imaging, tomography, unknown volumetric velocity distribution, bounded domain, full-wave data, 3-D geometry, rigorous multigrid-based forward approach, finite-difference time-domain method, nested finite element grid structure, multigrid-based inversion algorithm, coarse-to-fine inversion scheme, asteroid interior reconstruction, inversion technique, single satellite recording backscattering data, peak-to-peak signal-to-noise ratio, deep interior part, volumetric radar imaging, mathematical methodology, computational methodology, radar tomography imaging, Image reconstruction, Solar system, Radar imaging, Permittivity, Tomography, Computational modeling, Multigrid methods, radio tomography, microw-ave tomography, asteroids, biomedical imaging",
author = "M. Takala and D. Us and S. Pursiainen",
year = "2018",
month = "6",
day = "1",
doi = "10.1109/TCI.2018.2811908",
language = "English",
volume = "4",
pages = "228--240",
journal = "IEEE Transactions on Computational Imaging",
issn = "2333-9403",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "2",

}

RIS (suitable for import to EndNote) - Lataa

TY - JOUR

T1 - Multigrid-Based Inversion for Volumetric Radar Imaging With Asteroid Interior Reconstruction as a Potential Application

AU - Takala, M.

AU - Us, D.

AU - Pursiainen, S.

PY - 2018/6/1

Y1 - 2018/6/1

N2 - This study concentrates on advancing mathematical and computational methodology for radar tomography imaging in which the unknown volumetric velocity distribution of a wave within a bounded domain is to be reconstructed. Our goal is to enable effective simulation and inversion of a large amount of full-wave data within a realistic 2-D or 3-D geometry. For propagating and inverting the wave, we present a rigorous multigrid-based forward approach that utilizes the finite-difference time-domain method and a nested finite element grid structure. We also introduce and validate a multigrid-based inversion algorithm that allows regularization of the unknown distribution through a coarse-to-fine inversion scheme. Using this approach, sparse signals can be effectively inverted, as the coarse fluctuations are reconstructed before the finer ones. Furthermore, the number of nonzero entries in the system matrix can be compressed and, thus, the inversion procedure can be speeded up. As the test scenario, we investigate satellite-based asteroid interior reconstruction. We use both full-wave and projected wave data and estimate the accuracy of the inversion under different error sources: noise and positioning inaccuracies. The results suggest that the present inversion technique allows recovering the interior with a single satellite recording backscattering data. Robust results can be achieved, when the peak-to-peak signal-to-noise ratio is above 10 dB. Furthermore, the robustness for the deep interior part can be enhanced if two satellites can be utilized in the measurements.

AB - This study concentrates on advancing mathematical and computational methodology for radar tomography imaging in which the unknown volumetric velocity distribution of a wave within a bounded domain is to be reconstructed. Our goal is to enable effective simulation and inversion of a large amount of full-wave data within a realistic 2-D or 3-D geometry. For propagating and inverting the wave, we present a rigorous multigrid-based forward approach that utilizes the finite-difference time-domain method and a nested finite element grid structure. We also introduce and validate a multigrid-based inversion algorithm that allows regularization of the unknown distribution through a coarse-to-fine inversion scheme. Using this approach, sparse signals can be effectively inverted, as the coarse fluctuations are reconstructed before the finer ones. Furthermore, the number of nonzero entries in the system matrix can be compressed and, thus, the inversion procedure can be speeded up. As the test scenario, we investigate satellite-based asteroid interior reconstruction. We use both full-wave and projected wave data and estimate the accuracy of the inversion under different error sources: noise and positioning inaccuracies. The results suggest that the present inversion technique allows recovering the interior with a single satellite recording backscattering data. Robust results can be achieved, when the peak-to-peak signal-to-noise ratio is above 10 dB. Furthermore, the robustness for the deep interior part can be enhanced if two satellites can be utilized in the measurements.

KW - finite difference time-domain analysis

KW - geometry

KW - inverse problems

KW - radar imaging

KW - tomography

KW - unknown volumetric velocity distribution

KW - bounded domain

KW - full-wave data

KW - 3-D geometry

KW - rigorous multigrid-based forward approach

KW - finite-difference time-domain method

KW - nested finite element grid structure

KW - multigrid-based inversion algorithm

KW - coarse-to-fine inversion scheme

KW - asteroid interior reconstruction

KW - inversion technique

KW - single satellite recording backscattering data

KW - peak-to-peak signal-to-noise ratio

KW - deep interior part

KW - volumetric radar imaging

KW - mathematical methodology

KW - computational methodology

KW - radar tomography imaging

KW - Image reconstruction

KW - Solar system

KW - Radar imaging

KW - Permittivity

KW - Tomography

KW - Computational modeling

KW - Multigrid methods

KW - radio tomography

KW - microw-ave tomography

KW - asteroids

KW - biomedical imaging

U2 - 10.1109/TCI.2018.2811908

DO - 10.1109/TCI.2018.2811908

M3 - Article

VL - 4

SP - 228

EP - 240

JO - IEEE Transactions on Computational Imaging

JF - IEEE Transactions on Computational Imaging

SN - 2333-9403

IS - 2

ER -