Abstract:The theory of compressive sensing (CS) asserts that an unknown signal x ∈ C N can be accurately recovered from m measurements with m N provided that x is sparse. Most of the recovery algorithms need the sparsity s = x 0 as an input. However, generally s is unknown, and directly estimating the sparsity has been an open problem. In this study, an estimator of sparsity is proposed by using Bayesian hierarchical model. Its statistical properties such as unbiasedness and asymptotic normality are proved. In the simu… Show more
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