Signal Processing 2019 DOI: 10.1016/j.sigpro.2019.07.003 View full text
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Zhiyong Zhou, Jun Yu

Abstract: Sparse recovery aims to reconstruct an unknown spare or approximately sparse signal from significantly few noisy incoherent linear measurements. As a kind of computable incoherence measure of the measurement matrix, q-ratio constrained minimal singular values (CMSV) was proposed in Zhou and Yu [1] to derive the performance bounds for sparse recovery. In this paper, we study the geometrical property of the q-ratio CMSV, based on which we establish new sufficient conditions for signal recovery involving both sp…

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