Compressive Sensing on Three Dimensional SFCW Ground-Penetrating Radar

Stepped-frequency continuous-wave ground-penetrating radars (SFCW GPRs) are characterized by relatively low data acquisition speed caused by stepwise slow scanning of the frequency spectrum. The improvement of the imaging speed in SFCW GPR mostly relies on reducing the data acquisition time. In this paper, a paradigm of compressive sensing (CS) is explored in order to minimize the acquisition time in three dimensional SFCW GPRs by reducing the amount of real-time acquired data below the Nyquist rate by the use of the likely spatial sparsity of the underground. The signal reconstruction is performed by using the greedy algorithm Orthogonal Matching Pursuit (OMP). Data set with synthetic underground mines is accurately reconstructed for compression ratios up to 85%; causing that the data acquisition time is reduced 6.67 times. In addition, the results show that for the data set of synthetic mines degraded by uniform noise, the proposed CS approach can be used for effective noise-removal filtering.