期刊
IEEE TRANSACTIONS ON SIGNAL PROCESSING
卷 61, 期 21, 页码 5298-5309出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSP.2013.2278814
关键词
Basis selection; Bayesian; compressed sensing; greedy algorithm; linear regression; matching pursuit; minimum mean-square error (MMSE) estimate; sparse reconstruction
资金
- Office of Competitive Research (OCRF) at King Abdullah University of Science and Technology (KAUST) [CRG\_R2\_13\_ALOU\_KAUST\_2]
- King Abdulaziz City for Science and Technology (KACST) through the Science & Technology Unit at King Fahd University of Petroleum & Minerals (KFUPM) [09-ELE763-04]
- National Science, Technology and Innovation Plan
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.
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