Decomposition approaches to separate clutter/background from buried object signatures

© 2017 IEEE. Ground penetrating radar measurements are dominated by the strong return from the ground interface and volume scattering from distributed subsurface in-homogeneities. Buried object detection performance can be improved if these clutter sources can be reduced relative to the scattering from the buried objects of interest. This paper applies two recently developed methods of separating a signal into a low-rank component (representing the background) and a sparse component (the buried object), robust principal component analysis (RPCA) and dynamic mode decomposition (DMD), to the problem of separating subsurface scattering anomalies from a slowly varying background. The algorithms are described and an example application to field-collected impulse GPR data is shown. The target-to-clutter ratio is significantly improved in the sparse component compare to that in the original data suggesting that these techniques are viable methods of suppressing surface clutter and distributed volumetric clutter