• DocumentCode
    36606
  • Title

    Distributed Compressed Sensing off the Grid

  • Author

    Zhenqi Lu ; Rendong Ying ; Sumxin Jiang ; Peilin Liu ; Wenxian Yu

  • Author_Institution
    Dept. of Electr. Eng., Shanghai Jiao Tong Univ., Shanghai, China
  • Volume
    22
  • Issue
    1
  • fYear
    2015
  • fDate
    Jan. 2015
  • Firstpage
    105
  • Lastpage
    109
  • Abstract
    This letter investigates the joint recovery of a frequency-sparse signal ensemble sharing a common frequency-sparse component from the collection of their compressed measurements. Unlike conventional arts in compressed sensing, the frequencies follow an off-the-grid formulation and are continuously valued in [0, 1]. As an extension of atomic norm, the concatenated atomic norm minimization approach is proposed to handle the exact recovery of signals, which is reformulated as a computationally tractable semidefinite program. The optimality of the proposed approach is characterized using a dual certificate. Numerical experiments are performed to illustrate the effectiveness of the proposed approach and its advantage over separate recovery.
  • Keywords
    compressed sensing; mathematical programming; minimisation; common frequency-sparse component; compressed measurement; concatenated atomic norm minimization approach; distributed compressed sensing; dual certificate characterization; frequency-sparse signal ensemble joint recovery; off-the-grid formulation; semidefinite program; Atomic clocks; Compressed sensing; Frequency measurement; Joints; Minimization; Polynomials; Signal resolution; Atomic norm; basis mismatch; compressed sensing; joint sparsity; semidefinite program;
  • fLanguage
    English
  • Journal_Title
    Signal Processing Letters, IEEE
  • Publisher
    ieee
  • ISSN
    1070-9908
  • Type

    jour

  • DOI
    10.1109/LSP.2014.2349904
  • Filename
    6880754