A New Method for GPR Clutter Suppression Based on Stationary Graph Signals Processing

Ground-penetrating radar (GPR) is a vital tool in the domain of nondestructive testing; however, its capability to accurately discern subsurface targets faces challenges from substantial background clutter. Current methods aimed at clutter suppression often leave residual clutter or distort the hyperbolic tails of target-scattered signals, particularly in heterogeneous soil conditions. This study endeavors to tackle the complexities of clutter suppression in practical scenarios. To this end, we introduce a novel framework for GPR clutter suppression using stationary graph signal (SGS) processing techniques. In our proposed approach, GPR B-scan images are treated as graph signals and transformed into the graph frequency domain via graph Fourier transform (GFT). This framework incorporates B-scan images containing both targets and clutter alongside clutter-only B-scan images gathered within the same testing environment. B-scan images featuring both targets and clutter serve as reference data samples for constructing a graph shift operator (GSO), with clutter and targets’ scattering signals interpreted as SGS. Following the establishment of weak SGSs with respect to the GSO, a variant of the graph-based Wiener filter tailored for GPR applications is applied to effectuate clutter suppression. Through our proposed SGS processing-based filtering method, clutter can be effectively suppressed, thereby facilitating the restoration of target scattering signals. Extensive experiments conducted on both numerical simulation data and field test data underscore the efficacy of the proposed approach, which can be further applied to the general nondestructive testing realm.