Sparse reconstruction using distribution agnostic bayesian matching pursuit
KAUST DepartmentElectrical Engineering Program
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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.
SponsorsThis 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.