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Covariance estimation from compressive samples has become particularly attractive for two main reasons. First, many applications do not require the signal itself, and second-order statistics are oftentimes sufficient. The resulting requirement on the sampling rate of the original signal can therefore be reduced. Second, signal recovery from compressive samples leads to underdetermined systems which...
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