Expectation-Maximization Bernoulli-Gaussian Approximate Message Passing

In this paper, the authors navigate the space between these two extremes by modeling the signal as i.i.d Bernoulli-Gaussian (BG) with unknown prior sparsity, mean, and variance, and the noise as zero-mean Gaussian with unknown variance, and they simultaneously reconstruct the signal while learning the prior signal and noise parameters. To accomplish this task, they embed the BG-AMP algorithm within an Expectation-Maximization (EM) framework. Numerical experiments confirm the excellent performance of their proposed EM-BG-AMP on a range of signal types.

Provided by: Ohio State University Topic: Mobility Date Added: Nov 2011 Format: PDF

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