Abstract
Long data collecting time is one of the bottlenecks of the stepped-frequency continuous-wave ground penetrating radar (SFCW- GPR). We discuss the applicability of the Compressive Sensing (CS) method to three dimensional buried point-like targets imaging for SFCW-GPR. It is shown that the image of the sparse targets can be reconstructed by solving a constrained convex optimization problem based on l1-norm minimization with only a small number of data from randomly selected frequencies and antenna scan positions, which will reduce the data collecting time. Target localization ability, performance in noise, the efiect of frequency bandwidth, and the efiect of the wave travel velocity in the soil are demonstrated by simulated data. Numerical results show that the presented CS method can reconstruct the point-like targets in the right position even with 10% additive Gaussian white noise and some wave travel velocity estimation error.
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