Efficient Implementation of GPR Data Inversion in Case of Spatially Varying Antenna Polarizations

Journal article (2018)

Authors

J. Wang Microwave Sensing, Signals & Systems - EEMCS
P.J. Aubry Microwave Sensing, Signals & Systems - EEMCS
Alexander Yarovoy Microwave Sensing, Signals & Systems - EEMCS
Research Group
Microwave Sensing, Signals & Systems (EEMCS) (TU Delft)
Copyright: © 2018 J. Wang, P.J. Aubry, Alexander Yarovoy
DOI
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https://doi.org/10.1109/TGRS.2017.2779788
Ground penetrating radar Microwave imaging Radiation pattern Ground penetrating radar (GPR) Radar antennas Antenna radiation patterns Nonuniform fast Fourier transform (NUFFT) Dipole antennas Green's function (GF) Microwave antenna arrays Rotated array.
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Published Date

2018

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Microelectronics

Research Group

Microwave Sensing, Signals & Systems

Abstract

Ground penetrating radar imaging from the data acquired with arbitrarily oriented dipole-like antennas is considered. To take into account variations of antenna orientations resulting in spatial rotation of antenna radiation patterns and polarizations of transmitted fields, the full-wave method that accounts for the near-, intermediate-, and far-field contributions to the radiation patterns is applied for image reconstruction, which is formulated as a linear inversion problem. Two approaches, namely, an interpolation-based method and a nonuniform fast Fourier transform-based method, are suggested to efficiently implement the full-wave method by computing exact Green's functions. The effectiveness and accuracy of the method proposed have been verified via both numerical simulations and experimental measurements, and significant improvement of the reconstructed image quality compared with the traditional scalar-wave-based migration algorithms is demonstrated. The results can be directly utilized by forward-looking microwave imaging sensors such as installed at tunnel boring machine or can be used for the observation matrix computation in regularization-based inversion algorithms.