Paper Title
Theoretical Stable Recovery Analysis Of Exactly And Approximatelyjoint-Sparse Signals

Abstract
Stable recovery is an important issue in compressed sensing and convex optimization is the most commonly used tool to ensure the stability. This article considers the stability of exactly/ approximately sparse signal recovery in the presence of noise solved by convex optimization, called Multiple measurement vectors Basic Pursuit DeNoising (M-BPDN). We propose an oracle inequality of recovery error of (M-BPDN). In addition, with this inequality, we find a violation of intuition that we usually expect higher performance with more information. Experiment results are presented to verify that our theoretical study meets the observation. Index Terms - Basic Pursuit Denoising, Compressed sensing, Exactly/Approximately joint-sparse, Multiple measurement vectors, Oracle inequality