Synthetic Aperture Radar Imaging Below a Random Rough Surface

Motivated by applications in unmanned aerial based ground penetrating radar for detecting buried landmines, we consider the problem of imaging small point like scatterers situated in a lossy medium below a random rough surface. Both the random rough surface and the absorption in the lossy medium significantly impede the target detection and imaging process. Using principal component analysis we effectively remove the reflection from the air-soil interface. We then use a modification of the classical synthetic aperture radar imaging functional to image the targets. This imaging method introduces a user-defined parameter, delta, which scales the resolution by delta $\sqrt{\delta }$ allowing for target localization with sub wavelength accuracy. Numerical results in two dimensions illustrate the robustness of the approach for imaging multiple targets. However, the depth at which targets are detectable is limited due to the absorption in the lossy medium. We study imaging methods for identifying point targets in a lossy medium below a random rough surfaceWe effectively remove ground bounce signals in measurements using the singular value decomposition of the measurement data matrixWe apply a transformation to Kirchhoff migration to obtain tunably high-resolution images of small targets