脉冲噪声背景下的联合稀疏恢复方法， 在不同背景下给出了测试结果-presents a robust solution for joint sparse

recovery (JSR) under impulsive noise. The unknown measurement

noise is endowed with the Student-t distribution, then a

novel Bayesian probabilistic model is proposed to describe the

JSR problem. To effectively recover the joint row sparse signal,

variational Bayes (VB) method is introduced for Bayesian theory

based JSR algorithms such that it overcomes the intractable

integrations inherent. Simulation results verify that the proposed

algorithm significantly outperforms the existing algorithms under

impulsive noise.