High resolution range profile with promoted sparsity and refined grid

Abstract

A high resolution range profile (HRRP) method via compressed sensing (CS) with re-weighted 11 minimization is proposed. Since the weighted CS model has a better representation of the true sparse description than that of the general 11 norm based CS model, the proposed compressive HRRP exhibits a remarkable suppression of the noise influence when dealing with a high background noise circumstance. To reduce the computational complexity, a refinement method is drawn. The experiment results demonstrate that, compared with classical CS, our improved CS model can achieve a higher resolution range profile while its computation time is relatively small.