Here is a new implementation:
A Fast Non-Gaussian Bayesian Matching Pursuit Method for Sparse Reconstruction by Mudassir Masood, and Tareq Y. Al-Naffouri. The abstract reads:
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.
An implementation is available here and will be featured on the compressive sensing big picture page.
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