Clutter Removal Method for GPR Based on Low-Rank and Sparse Decomposition With Total Variation Regularization

Abstract

The performance of ground penetrating radar (GPR) target detection is seriously affected by the clutter. In this letter, an effective GPR clutter removal method is proposed based on low-rank and sparse decomposition with total variation regularization (LRSD-TVR). In the proposed method, a total variation (TV) regularization of sparse matrix is introduced to further remove the remaining clutter and to obtain a clearer target image. An iterative approach based on alternating direction method of multipliers (ADMM) is developed to solve the optimization problem of LRSD-TVR. In each iteration, the low rank component which corresponds to the clutter is computed by singular value decomposition (SVD) thresholding. Besides, the sparse component corresponding to the target is obtained by solving the sub-optimization problem reformulated in terms of TV component. The effectiveness of proposed method is verified by both numerical simulations and field experiments.