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Bayesian Compressive Sensing Using Normal Product Priors
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DOI：10.1109/LSP.2014.2364255.png)
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摘要
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In this letter, we introduce a new sparsity-promoting prior, namely, the normal product prior, and develop an efficient algorithm for sparse signal recovery under the Bayesian framework. The normal product distribution is the distribution of a product of two normally distributed variables with zero means and possibly different variances. Like other sparsity-encouraging distributions such as the Student's t-distribution, the normal product distribution has a sharp peak at the origin, which makes it a suitable prior to encourage sparse solutions. A two-stage normal product-based hierarchical model is proposed. We resort to the variational Bayesian (VB) method to perform the inference. Simulations are conducted to illustrate the effectiveness of our proposed algorithm as compared with other state-of-the-art compressed sensing algorithms.
Keyword:
Compressed Sensing
normal product prior
sparse Bayesian learning
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期刊
IF:
9.6
论文数:
1.1W
被引数:
1.7W
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