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• W2057202367 abstract "A fast matching pursuit method using a Bayesian approach is introduced for block-sparse signal recovery. This method performs Bayesian estimates of block-sparse signals even when the distribution of active blocks is non-Gaussian or unknown. It is agnostic to the distribution of active blocks in the signal and utilizes a priori statistics of additive noise and the sparsity rate of the signal, which are shown to be easily estimated from data and no user intervention is required. The method requires a priori knowledge of block partition and 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 block-sparse signal. Simulation results demonstrate the power and robustness of our proposed estimator." @default.
• W2057202367 created "2016-06-24" @default.
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• W2057202367 date "2013-05-01" @default.
• W2057202367 modified "2023-09-26" @default.
• W2057202367 title "Support agnostic Bayesian matching pursuit for block sparse signals" @default.
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• W2057202367 doi "https://doi.org/10.1109/icassp.2013.6638540" @default.
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