Ranked sparse signal support detection

Alyson K. Fletcher, Sundeep Rangan, Vivek K. Goyal

Research output: Contribution to journalArticlepeer-review


This paper considers the problem of detecting the support (sparsity pattern) of a sparse vector from random noisy measurements. Conditional power of a component of the sparse vector is defined as the energy conditioned on the component being nonzero. Analysis of a simplified version of orthogonal matching pursuit (OMP) called sequential OMP (SequOMP) demonstrates the importance of knowledge of the rankings of conditional powers. When the simple SequOMP algorithm is applied to components in nonincreasing order of conditional power, the detrimental effect of dynamic range on thresholding performance is eliminated. Furthermore, under the most favorable conditional powers, the performance of SequOMP approaches maximum likelihood performance at high signal-to-noise ratio.

Original languageEnglish (US)
Article number6241445
Pages (from-to)5919-5931
Number of pages13
JournalIEEE Transactions on Signal Processing
Issue number11
StatePublished - 2012


  • Compressed sensing
  • convex optimization
  • lasso
  • maximum likelihood estimation
  • orthogonal matching pursuit
  • random matrices
  • sparse Bayesian learning
  • sparsity
  • thresholding

ASJC Scopus subject areas

  • Signal Processing
  • Electrical and Electronic Engineering


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