A statistical approach to rough surface underground imaging

We develop a statistical approach for imaging weak-contrast objects buried under a rough soil/air interface using data from a ground penetrating radar array. The imaging system uses short-pulse electromagnetic plane waves as illumination signals. We develop an approximate physical model based on a flat soil/air interface and weak-scattering to relate the collected data to the buried objects. This model uses ray optics to represent the soil/air interface, and a Born approximation to model the weak contrast backscattering from the buried objects. In order to form images of objects buried under rough soil/air interfaces, we embed our model in a statistical framework to account for modeling errors and ill-posedness. The statistical framework leads to optimal reconstruction algorithms for buried objects. We evaluate the performance of the reconstruction algorithms for different soil/air interface realizations using data generated from detailed nonlinear electromagnetic simulations of ground penetrating radar