Compressive sensing for subsurface imaging using ground penetrating radar

摘要

The theory of compressive sensing (CS) enables the reconstruction of sparse signals from a small set of non-adaptive linear measurements by solving a convex e_1 minimization problem. This paper presents a novel data acquisition system for wideband synthetic aperture imaging based on CS by exploiting sparseness of point-like targets in the image space. Instead of measuring sensor returns by sampling at the Nyquist rate, linear projections of the returned signals with random vectors are used as measurements. Furthermore, random sampling along the synthetic aperture scan points can be incorporated into the data acquisition scheme. The required number of CS measurements can be an order of magnitude less than uniform sampling of the space-time data. For the application of underground imaging with ground penetrating radars (GPR), typical images contain only a few targets. Thus we show, using simulated and experimental GPR data, that sparser target space images are obtained which are also less cluttered when compared to standard imaging results.