Sparse Reconstruction Using Distribution Agnostic Bayesian Matching Pursuit

作者：Masood Mudassir^*; Al Naffouri Tareq Y

来源：IEEE Transactions on Signal Processing, 2013, 61(21): 5298-5309.

DOI：10.1109/TSP.2013.2278814

• Basis selection
• Bayesian
• compressed sensing
• greedy algorithm
• linear regression
• matching pursuit
• minimum mean-square error (MMSE) estimate
• sparse reconstruction

A fast matching pursuit method using a Bayesian approach is introduced for sparse signal recovery. This method 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. The method 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.

• 出版日期2013-11
• 单位中国石油大学（北京）