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dc.contributor.authorMasood, Mudassir
dc.contributor.authorAl-Naffouri, Tareq Y.
dc.date.accessioned2015-08-03T11:34:58Z
dc.date.available2015-08-03T11:34:58Z
dc.date.issued2013-11
dc.identifier.issn1053587X
dc.identifier.doi10.1109/TSP.2013.2278814
dc.identifier.urihttp://hdl.handle.net/10754/563065
dc.description.abstractA 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.
dc.description.sponsorshipThis 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.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttp://arxiv.org/abs/arXiv:1206.4208v1
dc.subjectBasis selection
dc.subjectBayesian
dc.subjectcompressed sensing
dc.subjectgreedy algorithm
dc.subjectlinear regression
dc.subjectmatching pursuit
dc.subjectminimum mean-square error (MMSE) estimate
dc.subjectsparse reconstruction
dc.titleSparse reconstruction using distribution agnostic bayesian matching pursuit
dc.typeArticle
dc.contributor.departmentElectrical Engineering Program
dc.identifier.journalIEEE Transactions on Signal Processing
dc.contributor.institutionDepartment of Electrical Engineering, King Fahd University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia
dc.identifier.arxividarXiv:1206.4208
kaust.personMasood, Mudassir
kaust.personAl-Naffouri, Tareq Y.


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