RIP Analysis for [Formula Omitted] ([Formula Omitted]) Minimization Method

Recently, non-convex and non-linear metrics have been introduced in compressed sensing to promote sparsity. This letter proposes an extension of the previously proposed [Formula Omitted] minimization method for sparse recovery using the [Formula Omitted] minimization method with [Formula Omitted]. We establish sufficient conditions for the [Formula Omitted] minimization to recover sparse signals under the restricted isometry property (RIP). Additionally, we develop an effective algorithm to solve the [Formula Omitted] minimization problem. Experiments show the proposed method is comparable to state-of-the-art methods for sparse signal recovery.