Exploration of innovative radar sensing schemes for subsurface object detection

The problem of abandoned landmines and unexploded ordnance is particularly acute when these objects are near the surface, so that their radar returns cannot easily be separated from the ground surface response. To address this, the authors pursue simulations designed to test methods of sensor deployment and data processing that exploit angular, positional, and frequency diversity for detection of metallic targets that are on the order of the subsurface wavelength in size. Rigorous 2D computations were performed and results processed for the angular correlation function (ACF) approach, in which one performs a coherent average of received signals from two incidence and observation angles. Simulations pursue the behavior of the ACF under realistic ground roughness and moisture content, target geometry, and highest practical resolution GPR frequencies. To achieve an expanded ensemble of cases, given a single subject ground surface, the authors average both over frequencies and overlapping incident beam locations