Compressive sensing for ground penetrating radar imaging based on random filtering

Sparse signals can be reconstructed from a small set of measurements basing on the theory of compressive sensing (CS), whereas the key points are the selection of the measurement matrix and the reconstruction algorithm. This paper presents an imaging algorithm for ground penetrating radar based on CS. The measurement matrix is selected via random filters, which can reduce the number of nonzero elements in the measurement matrix effectively. We adopt the simple orthogonal matching pursuit (OMP) algorithm to reconstruct signal with less data storage and lower computational complexity. Simulation results are provided to illustrate the performance of the proposed method.