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Ground penetrating radar (GPR) is a remote geophysical sensing method that has been applied in the localization of underground utilities, bridge deck survey, localization of landmines, mapping of terrain for aid in driverless cars, etc. Multistatic GPR can deliver a faster survey, wider spatial coverage, and multiple viewpoints of the subsurface. However, because of the transmit and receive antennas spatial offset, formation of 3D GPR image by simple stacking of the acquired A-scans is inaccurate. Also, averaging of different receivers data may lead to destructive interference of back-scattered waves due to different time delays implied by the spatial offset, so averaging does not lead to higher SNR in general. Furthermore, the energy back-scattered by scatter points are spread in hyperbolas in the GPR raw data. Migration or imaging algorithms are employed to increase SNR by focusing the hyperbolas. This focusing process also leads to better accuracy in target localization. In this paper, a computationally efficient synthetic aperture radar (SAR) imaging algorithm that properly integrates multistatic GPR data in both ground and air-coupled cases is presented. The algorithm is successfully applied on two synthetic datasets.
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This content will become publicly available on May 28, 2026
Enhanced GPR 3D SAR imaging using sparse signal recovery and back-projection algorithms for spatially random samplings
Ground Penetrating Radar (GPR) is essential for subsurface exploration. Conventional GPR 3D imaging demands dense spatial sampling along regular grids, which is both time-consuming and impractical in complex environments. In this work, we propose a novel method that combines sparse recovery techniques with a placement matrix to merge arbitrarily and sparsely sampled measurements into a regular grid framework. By exploiting the inherent sparsity of subsurface targets and using the Dantzig Selector with cross-validation, our method reconstructs the target reflectivity vector from random spatial sampling. The recovered data is then processed via the Back-Projection Algorithm (BPA) to generate high-resolution 3D images. Simulations demonstrate that our approach not only improves imaging quality under reduced sampling conditions but also efficiently handles arbitrary scanning paths by mapping irregular measurements onto the desired grid.
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- Award ID(s):
- 2345851
- PAR ID:
- 10636443
- Editor(s):
- Zelnio, Edmund; Garber, Frederick D
- Publisher / Repository:
- SPIE
- Date Published:
- ISBN:
- 9781510687011
- Page Range / eLocation ID:
- 22
- Subject(s) / Keyword(s):
- Ground Penetrating Radar, Sparse Spatial Sampling, 3D Imaging, Sparse Recovery, Placement Matrix, Dantzig Selector, Back-Projection Algorithm, Arbitrary Path Scanning
- Format(s):
- Medium: X
- Location:
- Orlando, United States
- Sponsoring Org:
- National Science Foundation
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