  A fast matching pursuit method using a Bayesian approach is introduced for
sparse signal recovery. This method, referred to as nGpFBMP, performs Bayesian
estimates of sparse signals even when the signal prior is non-Gaussian or
unknown. It is agnostic on signal statistics and utilizes a priori statistics
of additive noise and the sparsity rate of the signal, which are shown to be
easily estimated from data if not available. nGpFBMP utilizes a greedy approach
and order-recursive updates of its metrics to find the most dominant sparse
supports to determine the approximate minimum mean square error (MMSE) estimate
of the sparse signal. Simulation results demonstrate the power and robustness
of our proposed estimator.
