TY - JOUR
T1 - Sparse reconstruction using distribution agnostic bayesian matching pursuit
AU - Masood, Mudassir
AU - Al-Naffouri, Tareq Y.
N1 - KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work was funded in part by a CRG2 grant CRG\_R2\_13\_ALOU\_KAUST\_2 from the Office of Competitive Research (OCRF) at King Abdullah University of Science and Technology (KAUST). The work of T.Y. Al-Naffouri was also supported by King Abdulaziz City for Science and Technology (KACST) through the Science & Technology Unit at King Fahd University of Petroleum & Minerals (KFUPM) through Project No. 09-ELE763-04 as part of the National Science, Technology and Innovation Plan.
PY - 2013/11
Y1 - 2013/11
N2 - 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. © 2013 IEEE.
AB - 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. © 2013 IEEE.
UR - http://hdl.handle.net/10754/563065
UR - http://arxiv.org/abs/arXiv:1206.4208v1
UR - http://www.scopus.com/inward/record.url?scp=84884862337&partnerID=8YFLogxK
U2 - 10.1109/TSP.2013.2278814
DO - 10.1109/TSP.2013.2278814
M3 - Article
SN - 1053-587X
VL - 61
SP - 5298
EP - 5309
JO - IEEE Transactions on Signal Processing
JF - IEEE Transactions on Signal Processing
IS - 21
ER -