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Sparse Reconstruction Using Distribution Agnostic Bayesian Matching Pursuit (2013)

Institute of Electrical and Electronics Engineers (IEEE)

In: IEEE Transactions on Signal Processing

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Publication Date: 2013-09-25

Description: 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.

Print ISSN: 1053-587X

Electronic ISSN: 1941-0476

Topics: Electrical Engineering, Measurement and Control Technology

Published by Institute of Electrical and Electronics Engineers (IEEE) on behalf of The IEEE Signal Processing Society.

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