Parameter Inversion Method of Multilayered Media Based on Off-Grid Sparse CMP Model With Refined Orthogonal Matching Pursuit

The common middle point (CMP) ground-penetrating radar (GPR) utilizes the time-delay information under different antenna separations to realize layered inversion. However, parameter inversion of multilayered media is mostly subject to heavy computational complexity and worst estimation error in the...

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Published in	IEEE transactions on geoscience and remote sensing Vol. 62; pp. 1 - 14
Main Authors	Liu, Renjie, Yang, Xiaopeng, Liao, Jiancheng, Qu, Xiaodong, Yin, Peng, Fathy, Aly E.
Format	Journal Article
Language	English
Published	New York IEEE 2024 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects	Algorithms Approximation Approximation method Atmospheric modeling Common middle point (CMP) compressed sensing (CS) Computational modeling Dictionaries Ground penetrating radar ground-penetrating radar (GPR) Information processing Interpolation Matched pursuit Matching Matching pursuit algorithms Mathematical models Media multilayered media orthogonal matching pursuit (OMP) Parameters Permittivity Radar Reflected waves Refraction Taylor series Wave propagation
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ISSN	0196-2892 1558-0644
DOI	10.1109/TGRS.2024.3358014

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Abstract	The common middle point (CMP) ground-penetrating radar (GPR) utilizes the time-delay information under different antenna separations to realize layered inversion. However, parameter inversion of multilayered media is mostly subject to heavy computational complexity and worst estimation error in the determination of refraction positions. To address the problems, an effective parameter inversion method is proposed based on off-grid sparse CMP model with refined orthogonal matching pursuit (OMP) for multilayered parameter inversion. In the proposed method, a sparse CMP signal model with accurate reflected wave propagation modeling is developed based on a refraction approximation method. An off-grid sparse CMP model is further constructed based on second-order Taylor expansion to overcome the deviation from the grid node. Then, a refined OMP algorithm based on compressed sensing (CS) is proposed with an off-grid optimization process to achieve accurate off-grid parameter inversion. Finally, the effectiveness of the proposed method is verified by simulations and experiments.
AbstractList	The common middle point (CMP) ground-penetrating radar (GPR) utilizes the time-delay information under different antenna separations to realize layered inversion. However, parameter inversion of multilayered media is mostly subject to heavy computational complexity and worst estimation error in the determination of refraction positions. To address the problems, an effective parameter inversion method is proposed based on off-grid sparse CMP model with refined orthogonal matching pursuit (OMP) for multilayered parameter inversion. In the proposed method, a sparse CMP signal model with accurate reflected wave propagation modeling is developed based on a refraction approximation method. An off-grid sparse CMP model is further constructed based on second-order Taylor expansion to overcome the deviation from the grid node. Then, a refined OMP algorithm based on compressed sensing (CS) is proposed with an off-grid optimization process to achieve accurate off-grid parameter inversion. Finally, the effectiveness of the proposed method is verified by simulations and experiments.
Author	Liu, Renjie Yang, Xiaopeng Yin, Peng Liao, Jiancheng Fathy, Aly E. Qu, Xiaodong
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SubjectTerms	Algorithms Approximation Approximation method Atmospheric modeling Common middle point (CMP) compressed sensing (CS) Computational modeling Dictionaries Ground penetrating radar ground-penetrating radar (GPR) Information processing Interpolation Matched pursuit Matching Matching pursuit algorithms Mathematical models Media multilayered media orthogonal matching pursuit (OMP) Parameters Permittivity Radar Reflected waves Refraction Taylor series Wave propagation
Title	Parameter Inversion Method of Multilayered Media Based on Off-Grid Sparse CMP Model With Refined Orthogonal Matching Pursuit
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