Bayesian Learning For The Type-3 Joint Sparse Signal Recovery

Wei Chen & Ian Wassell
Compressed sensing (CS) is a signal acquisition paradigm that utilises the finding that a small number of linear projections of a sparse signal have enough information for stable recovery. This paper develops a Bayesian CS algorithm to simultaneously recover multiple signals that follow the Type-3 joint sparse model, where signals share a non-sparse common component and have distinct sparse innovation components. By employing the expectation-maximization (EM) algorithm, the proposed algorithm iteratively updates the estimates of...
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