Locally Adaptive Detection Algorithm for Forward-Looking Ground-Penetrating Radar

2011 ◽  
Author(s):  
Tim C. Havens ◽  
Dominic K. Ho ◽  
Justin Farrell ◽  
James M. Keller ◽  
Mihail Popescu ◽  
...  
2010 ◽  
Author(s):  
Timothy C. Havens ◽  
K. C. Ho ◽  
Justin Farrell ◽  
James M. Keller ◽  
Mihail Popescu ◽  
...  

2015 ◽  
Author(s):  
Darren Shaw ◽  
K. C. Ho ◽  
Kevin Stone ◽  
James M. Keller ◽  
Mihail Popescu ◽  
...  

2012 ◽  
Author(s):  
Timothy C. Havens ◽  
Kevin Stone ◽  
Derek T. Anderson ◽  
James M. Keller ◽  
K. C. Ho ◽  
...  

Electronics ◽  
2020 ◽  
Vol 9 (3) ◽  
pp. 541 ◽  
Author(s):  
Željko Bugarinović ◽  
Lara Pajewski ◽  
Aleksandar Ristić ◽  
Milan Vrtunski ◽  
Miro Govedarica ◽  
...  

This paper focuses on the use of the Canny edge detector as the first step of an advanced imaging algorithm for automated detection of hyperbolic reflections in ground-penetrating radar (GPR) data. Since the imaging algorithm aims to work in real time; particular attention is paid to its computational efficiency. Various alternative criteria are designed and examined, to fasten the procedure by eliminating unnecessary edge pixels from Canny-processed data, before such data go through the subsequent steps of the detection algorithm. The effectiveness and reliability of the proposed methodology are tested on a wide set of synthetic and experimental radargrams with promising results. The finite-difference time-domain simulator gprMax is used to generate synthetic radargrams for the tests, while the real radargrams come from GPR surveys carried out by the authors in urban areas. The imaging algorithm is implemented in MATLAB.


2020 ◽  
Vol 12 (5) ◽  
pp. 857 ◽  
Author(s):  
Davide Comite ◽  
Fauzia Ahmad ◽  
Traian Dogaru ◽  
Moeness Amin

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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