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Online dictionary learning aided target recognition in cognitive GPR

 
: Giovanneschi, F.; Mishra, K.V.; Gonzalez-Huici, M.A.; Eldar, Y.C.; Ender, J.H.G.

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Institute of Electrical and Electronics Engineers -IEEE-; IEEE Geoscience and Remote Sensing Society:
IEEE International Geoscience & Remote Sensing Symposium 2017. Proceedings : July 23-28, 2017, Fort Worth, Texas, USA
Piscataway, NJ: IEEE, 2017
ISBN: 978-1-5090-4951-6
ISBN: 978-1-5090-4950-9
ISBN: 978-1-5090-4952-3
S.4813-4816
International Geoscience and Remote Sensing Symposium (IGARSS) <37, 2017, Fort Worth/Tex.>
Englisch
Konferenzbeitrag
Fraunhofer FHR ()

Abstract
Sparse decomposition of ground penetration radar (GPR) signals facilitates the use of compressed sensing techniques for faster data acquisition and enhanced feature extraction for target classification. In this paper, we investigate use of an online dictionary learning (ODL) technique in the context of GPR to bring down the learning time as well as improve identification of abandoned anti-personnel landmines. Our experimental results using real data from an L-band GPR for PMN/PMA2, ERA and T72 mines show that ODL reduces learning time by 94% and increases clutter detection by 10% over the classical K-SVD algorithm. Moreover, our methods could be helpful in cognitive operation of the GPR where the system adapts the range sampling based on the learned dictionary.

: http://publica.fraunhofer.de/dokumente/N-502466.html