RNMF-Guided Deep Network for Signal Separation of GPR Without Labeled Data

Abstract

The clutter encountered in the ground-penetrating radar (GPR) system severely obscures the visibility of subsurface objects, especially in the case of overlapping target responses and clutter. In this letter, a novel self-supervised learning strategy with dual-network architecture and pseudolabels is proposed. First, the dual-network consists of two subnetworks: one is to simulate the low-rank part, and another simulates the sparse part. Second, the raw GPR data are decomposed as the sum of low-rank and sparse matrices by robust nonnegative matrix factorization (RNMF), termed two pseudolabels. Then, these pseudolabels guide the two subnetworks to accurately reconstruct the target response and clutter trace by trace, respectively. Results based on simulated data by gprMax and real datasets demonstrate that the proposed method is effective in separating target response from clutter and can achieve a similar effect as RNMF in less time without any prior information.