Imaging Modes for Ground Penetrating Radar and Their Relation to Detection Performance

Journal Article · Mon Oct 13 00:00:00 EDT 2014 · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing

DOI:https://doi.org/10.1109/JSTARS.2014.2357718· OSTI ID:1481055

Paglieroni, David W. ^[1]; Chambers, David H. ^[1]; Mast, Jeffrey E. ^[2]; Bond, Steven W. ^[1]; Reginald Beer, N. ^[1]

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1. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
2. Teres Technologies, Inc., Loveland, CO (United States)

The focus of this paper is an empirical study conducted to determine how imaging modes for ground penetrating radar (GPR) affect buried object detection performance. GPR data was collected repeatedly over lanes whose buried objects were mostly non-metallic. This data was collected and processed with a GPR antenna array, system hardware, and processing software developed by the authors and their colleagues. The system enables GPR data to be collected, imaged, and processed in real-time on a moving vehicle. The images are focused by applying multistatic and synthetic aperture imaging techniques either separately or jointly to signal scans acquired by the GPR antenna array. An image-based detection statistic derived from the ratio of buried object energy in the foreground to energy of soil in the background is proposed. Detection – false alarm performance improved significantly when the detection algorithm was applied to focused multistatic synthetic aperture radar (SAR) images rather than to unfocused GPR signal scans.

• Research Organization: Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
• Sponsoring Organization: USDOE National Nuclear Security Administration (NNSA)
• Grant/Contract Number: AC52-07NA27344
• OSTI ID: 1481055
• Report Number(s): LLNL-JRNL-649637; 769555
• Journal Information: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 8, Issue 3; ISSN 1939-1404
• Publisher: IEEE
• Country of Publication: United States
• Language: English

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