Prof. Forrest Sheng Bao Profile

Prof. Forrest Sheng Bao

at Iowa State Univ of Science and Technology

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Proceedings Article | 12 April 2021 Presentation

Target depth-based adaptive scanning in microwave synthetic aperture radar imaging for NDE applications

Chao Liu, Al Qaseer Mohammad Tayeb, Forrest Sheng Bao, Reza Zoughi

Proceedings Volume 11745, 1174509 (2021) https://doi.org/10.1117/12.2585930

KEYWORDS: Synthetic aperture radar, Nondestructive evaluation, Strategic intelligence, Radar imaging, Nonuniform sampling, Image quality

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Synthetic aperture radar (SAR) imaging has been widely used for various nondestructive evaluation (NDE) applications. The sampling strategy used to collect imaging data has great implications on the resulting image quality. The most widely-used strategies include uniform sampling and nonuniform sampling. While the former can provide relatively higher resolution and lower noise level, the latter can provide faster scanning time. However, applying uniform sampling for high resolution can be a critical issue when scanning a relatively large area. Moreover, neither of them takes target properties (e.g., depth, spatial distribution, etc.) directly into account. It has been verified that the optimum SAR resolution is target depth dependent, which means SAR intrinsically has lower resolution for targets at larger depths. This indicates that the sampling step can be accordingly increased for targets at large depths with little resolution degradation. Meanwhile, if the scene under test is relatively large and the flaws (usually just a few) are located in a relatively small region, then optimum uniform sampling over the entire large aperture, rather than a smaller area directly above the targets, may be unnecessary. Thus, first estimating target distribution density can help reduce the time in collecting imaging data. Consequently, an intelligent sampling strategy, with considerations of targets properties, is highly desired and investigated in this paper.

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