MCA-Based Clutter Reduction From Migrated GPR Data of Shallowly Buried Point Target

Reducing clutter is an important process in terms of improving the detection of targets when using ground penetrating radar (GPR), especially in the case of overlapping target responses and clutter. We present an alternative scheme to remove clutter from monostatic GPR data by combining migration and morphology component analysis (MCA). A simple phase shift migration is first performed on received B-scan data. In the migrated B-scan, clutter, and useful target responses exhibit different morphologies. The clutter appears to be horizontal stripes and the hyperbolic reflection from the target is refocused into an enhanced point-like shape. Subsequently, an overcomplete dictionary is constructed to discriminatively and sparsely represent each morphology based on the simulated GPR data. In particular, the 2-D undecimated discrete wavelet transform is used for the target component, and the 2-D curvelet atoms are used for the clutter. Then, the MCA technique is exploited to sparsely separate the two different components, which leads to the clutter reduction. The sparse representation of 2-D finite-difference-time-domain-based synthetic GPR data illustrates the validity of the overcomplete dictionary. In this paper, the proposed clutter removal method is tested on a synthetic model of point-like targets and real data from buried landmines. The migration concentrates the refracted signals from landmines to their origins, and the shapes of the landmines can be clearly defined. The results of the clutter reduction suggest that the proposed method is more effective than the method implemented directly on the B-scan data and the traditional mean subtraction.