Abstract
This paper analyzes the performance of the orthogonal matching pursuit (OMP) algorithm in recovering sparse signals from noisy measurement. Considering the fact that some matrices satisfy some restricted isometry properties (RIPs) but not the coherence condition, a superior RIP-based condition is proposed, which means that if the measurement matrix satisfies √k+1 < 1/(2 + √k) and the minimum component signal-to-noise ratio (MCSNR) is bounded, the OMP algorithm can exactly identify the support of the original sparse signal within k iterations. Finally, the theoretical results are verified by numerical simulations concerning different values of MCSNR and noise levels.
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