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Least Support Orthogonal Matching Pursuit Algorithm with Prior Information

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Języki publikacji
EN
Abstrakty
EN
This paper proposes a new fast matching pursuit technique named Partially Known Least Support Orthogonal Matching Pursuit (PKLS-OMP) which utilizes partially known support as a prior knowledge to reconstruct sparse signals from a limited number of its linear projections. The PKLS-OMP algorithm chooses optimum least part of the support at each iteration without need to test each candidate independently and incorporates prior signal information in the recovery process. We also derive sufficient condition for stable sparse signal recovery with the partially known support. Result shows that inclusion of prior information weakens the condition on the sensing matrices and needs fewer samples for successful reconstruction. Numerical experiments demonstrate that PKLS-OMP performs well compared to existing algorithms both in terms of reconstruction performance and execution time.
Rocznik
Strony
111--134
Opis fizyczny
Bibliogr. 14 poz., rys.
Twórcy
  • Electric and Electronic Engineering Department, Gaziantep University, Turkey
autor
  • Electric and Electronic Engineering Department, Gaziantep University, Turkey
Bibliografia
  • 1. Wei Dai and Olgica Milenkovic, “Subspace Pursuit for Compressive Sensing: Closing the Gap Between Performance and Complexity”, International Journal of Electronics and Computer Science Engineering 2010.
  • 2. Entao Liu and V.N. “Orthogonal Super Greedy Algorithm and Applications in Compressed Sensing”, IEEE Trans. Inform. Theory 58 (4), 2040-2047, 2011.
  • 3. D. Needell and J. A. Tropp, ”COSAMP: Iterative signal recovery from incomplete and inaccurate sanples”,Elsevier Appl. Comput. Harmon. Anal. 26 301–321 Volume 26, Issue 3, 2009.
  • 4. Wei Dai, Member, “Subspace Pursuit for Compressive Sensing Signal Reconstruction”, IEEE, and Olgica Milenkovic, Member, IEEE, 5, May 2009.
  • 5. Jing Meng, Lihong V. Wang, Leslie Ying, Dong Liang, Liang Song, “Compressed-sensing photoacoustic computed tomography in vivo with partial known support”, Optics Express, 20(13), pp. 16510-16523, 2012.
  • 6. N.Vaswani and W.Lu, “Modified-CS: Modifying compressive sensing for problems with partially known support,” in Proceedings, IEEE Int. Symp. Info. Theory, 2009.
  • 7. R. E. Carrillo, L. F. Polania, and K. E. Barner, “Iterative algorithms for compressed sensing with partially known support,” in Proceedings, IEEE Int. Conf. on Acoustics, Speech, and Signal Processing, Dallas, TX, Mar. 2010.
  • 8. Rafael E. Carrillo, Kenneth E. Barner, “Lorentzian Iterative Hard Thresholding: Robust Compressed Sensing with Prior Information”, IEEE Transactions on Signal Processing, Vol. 61, No. 19, pp 4822 - 4833. Oct. 2013.
  • 9. Tony Tony Cai, Lie Wang, and Guangwu Xu, “ Stable Recovery of Sparse Signals and an Oracle Inequality “, IEEE Transactions On Information Theory, Vol. 56, No. 7, July 2010.
  • 10.Jian Wang and Byonghyo Shim , “A Simple Proof of the Mutual Incoherence Condition for Orthogonal Matching Pursuit”, arXiv 1105.4408v1 [cs.It], 23 May 2011.
  • 11.Richard Baraniuk, “Compressive sensing ”, IEEE Signal Processing Magazine, 24(4), pp. 118-121, July 2007.
  • 12. Joel A. Tropp, and Anna C. Gilber, “Signal Recovery From Random Measurements Via Orthogonal Matching Pursuit”, IEEE Transaction on Information theory, Vol. 53, No. 12, December 2007.
  • 13. Parichat Sermwuthisarn, Supatana Auethavekiat, “Robust reconstruction algorithm for compressed sensing in Gaussian noise environment using orthogonal matching pursuit with partially known support and random subsampling”, EURASIP Journal on Advances in Signal Processing 2012.
  • 14.Parichat Sermwuthisarn, Supatana Auethavekiat, “Impulsive noise rejection method for compressed measurement signal in compressed sensing”,EURASIP Journal on Advances in Signal Processing 2012.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-28d8aff8-ad05-4f4c-9165-11adaf94fed7
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