Robust matching pursuit for recovery of Gaussian sparse signal

For compressive sensing (CS) recovery of Gaussian sparse signal, we explore the framework of Bayesian linear models to achieve a robust reconstruction performance in the presence of measurement noise. Using a priori statistical knowledge, we develop a minimum mean square error (MMSE) estimation based iterative greedy search algorithm. Through experimental evaluations, we show that the new algorithm provides a robust CS reconstruction performance compared to an existing least square based algorithm.