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Proceedings Paper

Modeling GPR data from lidar soil surface profile
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Paper Abstract

Ground Penetrating Radar (GPR) has been applied for several years to the problem of detecting both anti-personnel and anti-tank landmines. One major challenge for reliable mine detection using GPR is removing the response from the ground. When the ground is flat this is a straightforward process. For the NIITEK GPR, the flat ground will show up as one of the largest responses and will be consistent across all the channels, making the surface simple to detect and remove. Typically, the largest responses from each channel, assumed to be the surface, are aligned in range and then zeroed out. When the ground is not flat, the response from the ground becomes more complicated making it no longer possible to just assume the largest response is from the ground. Also, certain soil surface features can create responses that look very similar to those of mines. To further complicate the ground removal process, the motion of the GPR antenna is not measured, making it impossible to determine if the ground or antenna is moving from just the GPR data. To address surface clutter issues arising from uneven ground, NVESD investigated profiling the soil surface with a LIDAR. The motion of both the LIDAR and GPR was tracked so the relative locations could be determined. Using the LIDAR soil surface profile, GPR data was modeled using a simplified version of the Physical Optics model. This modeled data could then be subtracted from the measured GPR data, leaving the response without the soil surface. In this paper we present a description and results from an experiment conducted with a NIITEK GPR and LIDAR over surface features and buried landmines. A description of the model used to generate the GPR response from the soil and the algorithm that was used to subtract the two provided. Mine detection performances using both GPR only and GPR with LIDAR algorithms are compared.

Paper Details

Date Published: 11 May 2012
PDF: 9 pages
Proc. SPIE 8357, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVII, 835712 (11 May 2012); doi: 10.1117/12.922806
Show Author Affiliations
Brian Burns, U.S. Army Night Vision & Electronic Sensors Directorate (United States)
William W. Clark, U.S. Army Night Vision & Electronic Sensors Directorate (United States)
Ian McMichael, U.S. Army Night Vision & Electronic Sensors Directorate (United States)


Published in SPIE Proceedings Vol. 8357:
Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVII
J. Thomas Broach; John H. Holloway, Editor(s)

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