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Proceedings Paper

Forward looking GPR sidelobe reduction using L1-norm minimization
Author(s): Brian Burns
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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 area of research is using Forward Looking GPR (FLGPR) to detect mines. While FLGPR has the advantage of standoff versus downward looking GPR, the responses from buried targets generally decrease while the responses from clutter increase. One source of clutter is from sidelobes and grating lobes caused by off-road clutter. As it is not possible to get a narrow beamwidth at the low frequencies required to get ground penetration, FLGPR receives responses from both on and off the road. Off-road clutter responses are often much stronger than the responses from buried mines. These off-road clutter objects can produce sidelobes that overlap with and obscure the responses from inroad targets. This becomes especially problematic if the antenna array spacing is not fine enough and grating lobes are formed. To reduce both the sidelobes and grating lobes, a technique using L1-norm minimization was tested. One advantage of this technique is it only requires a single aperture. The resulting image retains phase information which allows the images to be then coherently summed, resulting in better quality images. In this paper a description of the algorithm is provided. The algorithm was applied to a FLGPR data set to show its ability to reduce both sidelobes and grating lobes. Resulting images are shown.

Paper Details

Date Published: 10 May 2012
PDF: 8 pages
Proc. SPIE 8357, Detection and Sensing of Mines, Explosive Objects, and Obscured Targets XVII, 835713 (10 May 2012); doi: 10.1117/12.922807
Show Author Affiliations
Brian Burns, 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 Jr., Editor(s)

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