Ground penetrating radar (GPR) using a 3D antenna array is employed for data acquisition over an area contaminated with landmine simulants. The performance of different processing methods applied in 1D, 2D and 3D radiograms are evaluated on real data obtained from two different test sites. Preliminary results are presented on the effectiveness of 3D GPR and on the applicability and limitations of widely used processing schemes.@en

A regression model is developed in order to estimate in real time the signal-to-clutter ratio (SCR) for landmine detection using ground-penetrating radar. Artificial neural networks are employed in order to express SCR with respect to the soil's properties, the depth of the target, and the central frequency of the pulse. The SCR is synthetically evaluated for a wide range of diverse and controlled scenarios using the finite-difference time-domain method. Fractals are used to describe the geometry of the soil's heterogeneities as well as the roughness of the surface. The dispersive dielectric properties of the soil are expressed with respect to traditionally used soil parameters, namely, sand fraction, clay fraction, water fraction, bulk density, and particle density. Through this approach, a coherent and uniformly distributed training set is created. The overall performance of the resulting nonlinear function is evaluated using scenarios which are not included in the training process. The calculated and the predicted SCR are in good agreement, indicating the validity and the generalization capabilities of the suggested framework.@en