Skip to main content
SpringerLink
Log in
Book cover

Proceedings of the 6th International Asia Conference on Industrial Engineering and Management Innovation pp 177–187Cite as

A Sparsity Adaptive Compressive Sampling Matching Pursuit Algorithm

  • Conference paper
  • First Online:

Abstract

In the area of compressed sensing (CS), the compressive sampling matching pursuit (CoSaMP) algorithm offers a theoretical reconstruction guarantee in noise environment by exploiting a backtracking framework. But it relies on the sparsity of signal. With a stage by stage reconstruction structure, the sparsity adaptive matching pursuit (SAMP) algorithm can reconstruct signals when the sparsity is unknown. By taking both advantages of CoSaMP and SAMP, we propose a greedy algorithm for reconstruction of sparse signals, called the sparsity adaptive compressive sampling matching pursuit (SACoSaMP). The proposed algorithm can reconstruct signals without prior information of sparsity, and it is robust to noise. In this paper, we give a residual upper bound of the proposed SACoSaMP algorithm, and demonstrate the performance of it through simulation experiments.

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   149.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD   189.00
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  1. Candès EJ, Romberg J, Tao T (2006) Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information. IEEE Trans Inf Theory 52(2):489–509

    Article  MATH  Google Scholar 

  2. Donoho DL (2006) Compressed sensing. IEEE Trans Inf Theory 52(4):1289–1306

    Article  MATH  MathSciNet  Google Scholar 

  3. Baraniuk RG (2007) Compressive sensing. IEEE Signal Process Mag 24(4):118–121

    Article  ADS  Google Scholar 

  4. Giryes R, Elad M (2012) Cosamp and SP for the cosparse analysis model. In: Proceedings of the 20th European signal processing conference, EUSIPCO´12, Bucharest, Rumania, pp 964–968

    Google Scholar 

  5. Candès EJ, Tao T (2005) Decoding by linear programming. IEEE Trans Inf Theory 51(12):4203–4215

    Article  MATH  Google Scholar 

  6. Candès EJ, Tao T (2006) Near-optimal signal recovery from random projections: universal encoding strategies? IEEE Trans Inf Theory 52(12):5406–5425

    Article  MATH  Google Scholar 

  7. Dai W, Milenkovic O (2009) Subspace pursuit for compressive sensing signal reconstruction. IEEE Trans Inf Theory 55(5):2230–2249

    Google Scholar 

  8. Mallat SG, Zhang Z (1993) Matching pursuits with time-frequency dictionaries. IEEE Trans Sign Process 41(12):3397–3415

    Article  MATH  ADS  Google Scholar 

  9. Tropp JA, Gilbert AC (2007) Signal recovery from random measurements via orthogonal matching pursuit. IEEE Trans Inf Theory 53(12):4655–4666

    Article  MATH  MathSciNet  Google Scholar 

  10. Donoho DL, Tsaig Y, Drori I, Starck J-L (2012) Sparse solution of underdetermined linear equations by stagewise orthogonal matching pursuit. IEEE Trans Inf Theory 58(2):1094–1121

    Article  MathSciNet  Google Scholar 

  11. Needell D, Vershynin R (2010) Signal recovery from incomplete and inaccurate measurements via regularized orthogonal matching pursuit. IEEE J Sel Top Sign Proces 4(2):310–316

    Google Scholar 

  12. Needell D, Tropp JA (2008) CoSaMP:iterative signal recovery from incomplete and inaccurate samples. Appl Comput Harmonic Anal 26(3):301–321

    Article  MathSciNet  Google Scholar 

  13. Giryes R, Elad M (2011) Denoising with greedy-like pursuit algorithms. In: Proceedings of the 19th European signal processing conference, EUSIPCO´11, Barcelona, Spain, pp 1475–1479

    Google Scholar 

  14. Do TT, Gan L, Nguyen N, Tran TD (2008) Sparsity adaptive matching pursuit algorithm for practical compressed sensing. In: Proceedings of 2008 42nd Asilomar conference on signals, system and computers, Pacific Grove, CA, pp 581–587

    Google Scholar 

  15. Cheng Y, Wei F, Hui F, Tao Y, Bo H (2010) A sparsity subspace pursuit algorithm for compressive sampling. Acta Electronica Sinica 38(8):1914–1917

    Google Scholar 

Download references

Acknowledgments

Project supported by the National Natural Science Foundation of China (No. 61271263).

Author information

Authors and Affiliations

  1. College of Information and Communication Engineering, Harbin Engineering University, Harbin, China

    Xiang-pu Liu & Li-li Guo

  2. Science and Technology on Electronic Information Control Laboratory, Chengdu, China

    Xiang-pu Liu, Feng Yang & Xiang Yi

Authors
  1. Xiang-pu Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Feng Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xiang Yi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Li-li Guo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Xiang-pu Liu .

Editor information

Editors and Affiliations

  1. Tianjin, China

    Ershi Qi

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Atlantis Press and the author(s)

About this paper

Cite this paper

Liu, Xp., Yang, F., Yi, X., Guo, Ll. (2016). A Sparsity Adaptive Compressive Sampling Matching Pursuit Algorithm. In: Qi, E. (eds) Proceedings of the 6th International Asia Conference on Industrial Engineering and Management Innovation. Atlantis Press, Paris. https://doi.org/10.2991/978-94-6239-145-1_18

Download citation

Publish with us

Policies and ethics

Search

Navigation