On Probability of Support Recovery for Orthogonal Matching Pursuit Using Mutual Coherence

Ehsan Miandji, Mohammad Emadi, Jonas Unger, Ehsan Afshari
IEEE Signal Processing Letters, Volume 24, Number 11, page 1646--1650 - November 2017
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In this paper we present a new coherence-based performance guarantee for the Orthogonal Matching Pursuit (OMP) algorithm. A lower bound for the probability of correctly identifying the support of a sparse signal with additive white Gaussian noise is derived. Compared to previous work, the new bound takes into account the signal parameters such as dynamic range, noise variance, and sparsity. Numerical simulations show significant improvements over previous work and a closer match to empirically obtained results of the OMP algorithm.

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See also

MATLAB source code for generating the results is here . The code relies on Ron Rubinstein's OMP toolbox. We have included the toolbox (or you can download it from here )

BibTex references

@Article\{MEUA17,
  author       = "Miandji, Ehsan and Emadi, Mohammad and Unger, Jonas and Afshari, Ehsan",
  title        = "On Probability of Support Recovery for Orthogonal Matching Pursuit Using Mutual Coherence",
  journal      = "IEEE Signal Processing Letters",
  number       = "11",
  volume       = "24",
  pages        = "1646--1650",
  month        = "November",
  year         = "2017",
  key          = "MEUA2017"
}

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