• DocumentCode
    1765298
  • Title

    Stable Signal Recovery via Randomly Enhanced Adaptive Subspace Pursuit Method

  • Author

    Lufeng Liu ; Xinpeng Du ; Lizhi Cheng

  • Author_Institution
    Dept. of Math. & Syst. Sci., Nat. Univ. of Defense Technol., Changsha, China
  • Volume
    20
  • Issue
    8
  • fYear
    2013
  • fDate
    Aug. 2013
  • Firstpage
    823
  • Lastpage
    826
  • Abstract
    This letter addresses the stable signal recovery problem in the presence of noisy perturbations, and presents a variant of Subspace Pursuit (SP) method, called Randomly Enhanced Adaptive SP (REASP) method. REASP first adds correlative and irrelative atoms with an adaptive technique and a random strategy respectively, next detects the overall reliability, and then deletes unreliable atoms per iteration. Through these modifications, REASP is expected to achieve superior performance. The complexity analysis shows that the computational complexity of REASP is a bit higher but acceptable. The experimental results illustrate the superior performance of REASP, compared to the frequently used greedy pursuit (GP) and l1 basis pursuit (BP) methods.
  • Keywords
    adaptive signal processing; computational complexity; signal reconstruction; REASP method; computational complexity; correlative atoms; irrelative atoms; randomly enhanced adaptive subspace pursuit method; stable signal recovery problem; Atomic measurements; Compressed sensing; Computational complexity; Matching pursuit algorithms; Noise measurement; Signal to noise ratio; Adaptive technique; compressed sensing; random strategy; stable signal recovery; subspace pursuit;
  • fLanguage
    English
  • Journal_Title
    Signal Processing Letters, IEEE
  • Publisher
    ieee
  • ISSN
    1070-9908
  • Type

    jour

  • DOI
    10.1109/LSP.2013.2267796
  • Filename
    6530669