It is shown that compressive sensing (CS) theory can be used for subsurface imaging in stepped frequency ground penetrating radars (GPR), resulting in robust sparse images, using fewer measurements. Although the data acquisition time is decreased by CS, the computational complexity of the minimization based imaging algorithm is too costly, which makes the algorithm useless, especially for extensive discretization or 3D imaging. In this paper, a greedy alternative, orthogonal matching pursuit (OMP) is used for imaging subsurface and its performance under various conditions is compared to CS imaging method. Results show that OMP could reconstruct sparse signals robustly as well as CS imaging. It is faster and easier to implement so it can be said that OMP is a fascinating alternative to CS imaging method for subsurface GPR imaging.
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