Signal Processing 2020 DOI: 10.1016/j.sigpro.2019.107335 View full text
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Sreejith Kallummil, Sheetal Kalyani

Abstract: Recovering the support of sparse vectors in underdetermined linear regression models, aka, compressive sensing is important in many signal processing applications. High SNR consistency (HSC), i.e., the ability of a support recovery technique to correctly identify the support with increasing signal to noise ratio (SNR) is an increasingly popular criterion to qualify the high SNR optimality of support recovery techniques. The HSC results available in literature for support recovery techniques applicable to unde…

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