Robust sparse recovery via a novel convex model

In this paper, we propose a novel convex model to recover the sparse signal through the proposed model in the framework of restricted isometry property without the knowledge of the noise type of the measurement model. In addition, several reliable numerical experiments are given to show that the new model has better recovery performance for signals with different noise compared with classical methods such as basis pursuit and Dantzig selector.