Modeling Buried Object Brightness and Visibility for Ground Penetrating Radar

Stevenson, Garrett A.; Wilson, Jason; Worthmann, Brian M.; Xavier, Wlamir

doi:10.1109/tgrs.2021.3084100

Modeling Buried Object Brightness and Visibility for Ground Penetrating Radar

Journal Article · Mon Jun 07 00:00:00 EDT 2021 · IEEE Transactions on Geoscience and Remote Sensing

DOI:https://doi.org/10.1109/tgrs.2021.3084100· OSTI ID:1808250

^[1]; Wilson, Jason ^[2]; ^[1]; Xavier, Wlamir ^[3]

Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
Biola Univ., La Mirada, CA (United States). Biola Quantitative Consulting Center; Biola Univ., La Mirada, CA (United States)
Biola Univ., La Mirada, CA (United States). Biola Quantitative Consulting Center

Comparing the observed brightness of various buried objects is a straightforward way to characterize the performance of a ground penetrating radar (GPR) system. However, a limitation arises. A simple comparison of buried object brightness values does not disentangle the effects of the GPR system itself from the system's operating environment and the objects being observed. Therefore, with brightness values exhibiting an unknown synthesis of systemic, environmental, and object factors, GPR system analysis becomes a convoluted affair. In this work, we use an experimentally collected dataset of over 25,000 object observations from five different multistatic radar arrays to develop models of buried object brightness and control for these various effects. Additionally, our modeling efforts provide a means for quantifying the relative brightness of GPR systems, the objects they detect, and the physical properties of those objects that influence observed brightness. To evaluate the models' performance on new object observations, we repeatedly simulate fitting them to half the dataset and predicting the observed brightness values of the unseen half. In addition, we introduce a method for estimating the probability that individual observations constitute a visible object, which aids in failure analysis, performance characterization, and dataset cleaning.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)

Sponsoring Organization:: USDOE National Nuclear Security Administration (NNSA)

Grant/Contract Number:: AC52-07NA27344

OSTI ID:: 1808250

Report Number(s):: LLNL-JRNL--818303; 1028394

Journal Information:: IEEE Transactions on Geoscience and Remote Sensing, Journal Name: IEEE Transactions on Geoscience and Remote Sensing Vol. 60; ISSN 0196-2892

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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Related Subjects

97 MATHEMATICS AND COMPUTING
brightness
buried object detection
explosive hazard detection
ground penetrating radar
measurement
radar
radar array comparison
radar imaging
statistical modeling
tomographic imaging
tomography
uncertainty

Modeling Buried Object Brightness and Visibility for Ground Penetrating Radar

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