Clutter Removal Method for Through-Wall Radar Based on Weighted Bilinear Factorization Regularization

When using ultra-wideband (UWB) radar to detect human bodies behind walls, reflections from the wall can completely obscure the life signals. To eliminate this clutter, a method utilizing the low-rank characteristics of the clutter and the sparsity of the life signals is proposed: a weighted bilinear decomposition regularization method. This approach employs nonconvex matrix factorization and weighting to replace the rank of the matrix, inheriting the computational efficiency of bilinear decomposition while incorporating the singular values of the empirical distribution. Additionally, total variation (TV) regularization of the sparse matrix is introduced to further remove the residual clutter, thereby obtaining clearer life signals behind the wall. The proposed model is optimized using the iterative method of alternating direction method of multipliers (ADMM). Experiments with single and multiple targets behind the wall demonstrate that this method effectively separates wall clutter from behind-the-wall targets, ensuring precise target detection. Compared to existing through-wall radar clutter suppression methods, it achieves a higher target-to-clutter ratio (TCR).