Table of Contents Author Guidelines Submit a Manuscript
Journal of Applied Mathematics
Volume 2014, Article ID 704231, 7 pages
http://dx.doi.org/10.1155/2014/704231
Research Article

A New Subband Adaptive Filtering Algorithm for Sparse System Identification with Impulsive Noise

Department of Electronic Engineering, Gangneung-Wonju National University, Gangneung 210-702, Republic of Korea

Received 13 March 2014; Revised 21 April 2014; Accepted 5 May 2014; Published 20 May 2014

Academic Editor: Guiming Luo

Copyright © 2014 Young-Seok Choi. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. S. Haykin, Adaptive Filter Theory, Prentice Hall, Upper Saddle River, NJ, USA, 4th edition, 2002.
  2. A. H. Sayed, Fundamentals of Adaptive Filtering, John Wiley & Sons, New York, NY, USA, 2003.
  3. P. S. R. Diniz, Adaptive Filtering: Algorithms and Practical Implementations, Kluwer Academic, Boston, Mass, USA, 3rd edition, 2008.
  4. M. M. Sondhi, “The history of echo cancellation,” IEEE Signal Processing Magazine, vol. 23, no. 5, pp. 95–102, 2006. View at Publisher · View at Google Scholar · View at Scopus
  5. A. Gilloire and M. Vetterli, “Adaptive filtering in subbands with critical sampling: analysis, experiments, and application to acoustic echo cancellation,” IEEE Transactions on Signal Processing, vol. 40, no. 8, pp. 1862–1875, 1992. View at Publisher · View at Google Scholar · View at Scopus
  6. M. de Courville and P. Duhamel, “Adaptive filtering in subbands using a weighted criterion,” IEEE Transactions on Signal Processing, vol. 46, no. 9, pp. 2359–2371, 1998. View at Publisher · View at Google Scholar · View at Scopus
  7. S. S. Pradhan and V. U. Redd, “A new approach to subband adaptive filtering,” IEEE Transactions on Signal Processing, vol. 47, no. 3, pp. 655–664, 1999. View at Publisher · View at Google Scholar · View at Scopus
  8. K. A. Lee and W. S. Gan, “Improving convergence of the NLMS algorithm using constrained subband updates,” IEEE Signal Processing Letters, vol. 11, no. 9, pp. 736–739, 2004. View at Publisher · View at Google Scholar · View at Scopus
  9. K. A. Lee and W. S. Gan, “Inherent decorrelating and least perturbation properties of the normalized subband adaptive filter,” IEEE Transactions on Signal Processing, vol. 54, no. 11, pp. 4475–4480, 2006. View at Publisher · View at Google Scholar · View at Scopus
  10. D. L. Duttweiler, “Proportionate normalized least-mean-squares adaptation in echo cancelers,” IEEE Transactions on Speech and Audio Processing, vol. 8, no. 5, pp. 508–518, 2000. View at Publisher · View at Google Scholar · View at Scopus
  11. W. Li and J. C. Preisig, “Estimation of rapidly time-varying sparse channels,” IEEE Journal of Oceanic Engineering, vol. 32, no. 4, pp. 927–939, 2007. View at Publisher · View at Google Scholar · View at Scopus
  12. W. F. Schreiber, “Advanced television systems for terrestrial broad-casting: some problems and some proposed solutions,” Proceedings of IEEE, vol. 83, no. 6, pp. 958–981, 1995. View at Publisher · View at Google Scholar
  13. S. L. Gay, “Efficient, fast converging adaptive filter for network echo cancellation,” in Proceedings of the 32nd Asilomar Conference on Signals, Systems & Computers, vol. 1, pp. 394–398, Pacific Grove, Calif, USA, November 1998. View at Scopus
  14. H. Deng and M. Doroslovački, “Improving convergence of the PNLMS algorithm for sparse impulse response identification,” IEEE Signal Processing Letters, vol. 12, no. 3, pp. 181–184, 2005. View at Publisher · View at Google Scholar · View at Scopus
  15. M. S. E. Abadi, “Proportionate normalized subband adaptive filter algorithms for sparse system identification,” Signal Processing, vol. 89, no. 7, pp. 1467–1474, 2009. View at Publisher · View at Google Scholar · View at Scopus
  16. L. R. Vega, H. Rey, J. Benesty, and S. Tressens, “A new robust variable step-size NLMS algorithm,” IEEE Transactions on Signal Processing, vol. 56, no. 5, pp. 1878–1893, 2008. View at Publisher · View at Google Scholar · View at MathSciNet
  17. Z. Yang, Y. R. Zheng, and S. L. Grant, “Proportionate affine projection sign algorithms for network echo cancellation,” IEEE Transactions on Audio, Speech and Language Processing, vol. 19, no. 8, pp. 2273–2284, 2011. View at Publisher · View at Google Scholar · View at Scopus
  18. B. Liao, Z. G. Zhang, and S. C. Chan, “A new robust Kalman filter-based subspace tracking algorithm in an impulsive noise environment,” IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 57, no. 9, pp. 740–744, 2010. View at Publisher · View at Google Scholar · View at Scopus
  19. J. Ni and F. Li, “Variable regularisation parameter sign subband adaptive filter,” Electronics Letters, vol. 46, no. 24, pp. 1605–1607, 2010. View at Publisher · View at Google Scholar · View at Scopus
  20. D. L. Donoho, “Compressed sensing,” IEEE Transactions on Information Theory, vol. 52, no. 4, pp. 1289–1306, 2006. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  21. E. J. Candès, J. Romberg, and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Transactions on Information Theory, vol. 52, no. 2, pp. 489–509, 2006. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  22. R. Tibshirani, “Regression shrinkage and selection via the lasso,” Journal of the Royal Statistical Society B: Methodological, vol. 58, no. 1, pp. 267–288, 1996. View at Google Scholar · View at Zentralblatt MATH · View at MathSciNet
  23. Y. Chen, Y. Gu, and A. O. Hero, “Sparse LMS for system identification,” in Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP '09), pp. 3125–3128, Taipei, Taiwan, April 2009. View at Publisher · View at Google Scholar · View at Scopus
  24. Y. Gu, J. Jin, and S. Mei, “l0 norm constraint LMS algorithm for sparse system identification,” IEEE Signal Processing Letters, vol. 16, no. 9, pp. 774–777, 2009. View at Publisher · View at Google Scholar · View at Scopus
  25. J. Jin, Y. Gu, and S. Mei, “A stochastic gradient approach on compressive sensing signal reconstruction based on adaptive filtering framework,” IEEE Journal on Selected Topics in Signal Processing, vol. 4, no. 2, pp. 409–420, 2010. View at Publisher · View at Google Scholar · View at Scopus
  26. E. M. Eksioglu and A. K. Tanc, “RLS algorithm with convex regularization,” IEEE Signal Processing Letters, vol. 18, no. 8, pp. 470–473, 2011. View at Publisher · View at Google Scholar · View at Scopus
  27. N. Kalouptsidis, G. Mileounis, B. Babadi, and V. Tarokh, “Adaptive algorithms for sparse system identification,” Signal Processing, vol. 91, no. 8, pp. 1910–1919, 2011. View at Publisher · View at Google Scholar · View at Scopus
  28. Y.-S. Choi, “Subband adaptive filtering with l1-norm constraint for sparse system identification,” Mathematical Problems in Engineering, vol. 2013, Article ID 601623, 7 pages, 2013. View at Publisher · View at Google Scholar · View at MathSciNet
  29. P. S. Bradley and O. L. Mangasarian, “Feature selection via concave minimization and support vector machines,” in Proceedings of the International Conference on Machine Learning (ICML '98), pp. 82–90, 1998.
  30. P. P. Vaidyanathan, Multirate Systems and Filterbanks, Prentice Hall, Englewood Cliffs, NJ, USA, 1993.