Deep belief networks for false alarm rejection in forward-looking ground-penetrating radar

2015 ◽  
Author(s):  
John Becker ◽  
Timothy C. Havens ◽  
Anthony Pinar ◽  
Timothy J. Schulz
Keyword(s):  
False Alarm ◽  
Ground Penetrating Radar ◽  
Belief Networks ◽  
Deep Belief Networks ◽  
Ground Penetrating ◽  
Forward Looking

Explosive hazard detection using MIMO forward-looking ground penetrating radar

2015 ◽  
Author(s):  
Darren Shaw ◽  
K. C. Ho ◽  
Kevin Stone ◽  
James M. Keller ◽  
Mihail Popescu ◽  
...  
Keyword(s):  
Ground Penetrating Radar ◽  
Hazard Detection ◽  
Ground Penetrating ◽  
Forward Looking

Multiple kernel learning for explosive hazard detection in forward-looking ground-penetrating radar

2012 ◽  
Author(s):  
Timothy C. Havens ◽  
Kevin Stone ◽  
Derek T. Anderson ◽  
James M. Keller ◽  
K. C. Ho ◽  
...  
Keyword(s):  
Ground Penetrating Radar ◽  
Multiple Kernel Learning ◽  
Kernel Learning ◽  
Multiple Kernel ◽  
Hazard Detection ◽  
Ground Penetrating ◽  
Forward Looking

Locally adaptive detection algorithm for forward-looking ground-penetrating radar

2010 ◽  
Author(s):  
Timothy C. Havens ◽  
K. C. Ho ◽  
Justin Farrell ◽  
James M. Keller ◽  
Mihail Popescu ◽  
...  
Keyword(s):  
Ground Penetrating Radar ◽  
Detection Algorithm ◽  
Adaptive Detection ◽  
Locally Adaptive ◽  
Ground Penetrating ◽  
Forward Looking

scholarly journals Coherence-Factor-Based Rough Surface Clutter Suppression for Forward-Looking GPR Imaging

2020 ◽  
Vol 12 (5) ◽  
pp. 857 ◽  
Author(s):  
Davide Comite ◽  
Fauzia Ahmad ◽  
Traian Dogaru ◽  
Moeness Amin
Keyword(s):  
Rough Surface ◽  
Ground Penetrating Radar ◽  
Tomographic Reconstruction ◽  
Matched Filtering ◽  
Tomographic Image ◽  
Clutter Suppression ◽  
Coherence Factor ◽  
Ground Penetrating ◽  
Enhanced Imaging ◽  
Forward Looking

We present an enhanced imaging procedure for suppression of the rough surface clutter arising in forward-looking ground-penetrating radar (FL-GPR) applications. The procedure is based on a matched filtering formulation of microwave tomographic imaging, and employs coherence factor (CF) for clutter suppression. After tomographic reconstruction, the CF is first applied to generate a “coherence map” of the region in front of the FL-GPR system illuminated by the transmitting antennas. A pixel-by-pixel multiplication of the tomographic image with the coherence map is then performed to generate the clutter-suppressed image. The effectiveness of the CF approach is demonstrated both qualitatively and quantitatively using electromagnetic modeled data of metallic and plastic shallow-buried targets.

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