Sparse Wavelet Transform Based on Weight Vector Iteration with Minimum L1 Norm for Ground Penetrating Radar

In this paper, we propose a new weighted L1 norm minimization algorithm, which requires a more relaxed constrained equidistant constant (RIC) boundary. The constrained L1 norm minimization can restore sparse solutions from a small number of linear observations, and the variable weight L1 norm minimizes It can effectively improve the mathematical performance of sparse solutions and produce a series of approximate solutions with strong convergence. Finally, this method is applied to the recovery of wavelet sparse matrix, and a good success rate and extremely low recovery error are obtained. It can be proved that the mathematical performance of the new algorithm proposed in this paper is better than other advanced methods in the field of compressed sensing.