The imaging of subsurface targets, such as landmines, using Ground
Penetrating Radar (GPR) is becoming an increasingly important area
of research. Conventional image formation techniques expend large
amounts of computational resources on fully resolving a region,
even if there is a large amount of clutter. For example, standard
backprojection algorithms require O(N3). However, by using
multi-resolution techniques-such as quadtree-potential targets and clutter can be discriminated more efficiently with O(N2log2N). Because prior work has focused on the imaging of surface targets, quadtree techniques have mostly been developed for 2D imaging. Target depth adds another dimension to the imaging problem; therefore, we have developed a 3D quadtree algorithm. In this case, the mine field is modeled as a volume that is sub-divided at each stage of the quadtree algorithm. From each of these sub-volumes, the energy intensity is calculated. As the algorithm proceeds to finer resolutions, the energy in region containing a potential target increases, while that of background noise decreases. A multi-stage detector applied on intermediate quadtree data uses this change in energy to discriminate between regions of targets and clutter. This is advantageous because only the regions containing likely targets are investigated by additional sensors that are relatively slow in comparison to GPR (e.g. seismic or EMI sensors). This algorithm is tested on synthetic and experimental data collected from a model mine field at Georgia Institute of Technology. Even under near field and small aperture conditions, which hold for the mine detection case, test results show that target location information can be gathered with processing using the 3D quadtree algorithm.