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Ground Penetrating Radar (GPR) is essential for subsurface exploration. Conventional GPR 3D imaging demands dense spatial sampling along regular grids, which is both time-consuming and impractical in complex environments. In this work, we propose a novel method that combines sparse recovery techniques with a placement matrix to merge arbitrarily and sparsely sampled measurements into a regular grid framework. By exploiting the inherent sparsity of subsurface targets and using the Dantzig Selector with cross-validation, our method reconstructs the target reflectivity vector from random spatial sampling. The recovered data is then processed via the Back-Projection Algorithm (BPA) to generate high-resolution 3D images. Simulations demonstrate that our approach not only improves imaging quality under reduced sampling conditions but also efficiently handles arbitrary scanning paths by mapping irregular measurements onto the desired grid.