Multi-resolution GPR clutter suppression method based on low-rank and sparse decomposition

Abstract

The clutter encountered in ground-penetrating radar (GPR) seriously affects the detection and identification for the subsurface target, which has been widely studied in recent years. A low-rank and sparse decomposition (LRSD) method with multi-resolution is introduced in this paper. First, the raw GPR data is decomposed by stationary wavelet transform (SWT) to obtain different sub-bands. Then, the robust non-negative matrix factorization (RNMF) is used for approximation sub-bands and horizontal wavelet sub-bands to extract the target sparse parts. Next, the wavelet soft threshold de-noising is used for the vertical and diagonal wavelet sub-bands. Finally, the inverse wavelet transform of processed sub-bands is performed to reconstruct the target signal. The proposed method is compared with the subspace method and LRSD methods on both simulation data and real collected data. Visual and quantitative results show that the proposed method has better clutter suppression performance.