Ground Penetrating Radar Imaging via Random Filtering

Ground penetrating radar (GPR) is an ultra-wideband radar system, whose echo signal requires a lot of space for storage if using the conventional Nyquist sampling. However, the theory of compressive sensing (CS) enables the reconstruction of sparse signals from a small set of measurements, whereas the key point is the selection of the measurement matrix and the reconstruction algorithm. This paper presents an imaging algorithm for GPR based on CS. The measurement matrix is selected via random filters, which effectively reduces the number of non-zero elements in the measurement matrix. The simple Orthogonal Matching Pursuit (OMP) algorithm is adopted to reconstruct signal with less data storage and lower computational complexity. The proposed algorithm can also apply for the data compressed in both the temporal and spatial domain. Finally, this paper presents a CS scheme for GPR receiver based on timecontinuous signal. Some simulation experiments are taken on testing the proposed method on GPR imaging, and the results are provided to illustrate the performance of this method.