Table of Contents
Journal of Stochastics
Volume 2014, Article ID 502406, 7 pages
http://dx.doi.org/10.1155/2014/502406
Research Article

Adaptive Algorithm for Multichannel Autoregressive Estimation in Spatially Correlated Noise

Electrical Engineering Department, Shahid Chamran University, Ahvaz, Iran

Received 24 April 2014; Accepted 5 June 2014; Published 19 June 2014

Academic Editor: Chi-Yi Tsai

Copyright © 2014 Alimorad Mahmoudi. 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. M. Kay, Modern Spectral Estimation: Theory and Application, Prentice-Hall, Englewood Cliffs, NJ, USA, 1988.
  2. S. Srinivasan, R. Aichner, W. B. Kleijn, and W. Kellermann, “Multichannel parametric speech enhancement,” IEEE Signal Processing Letters, vol. 13, no. 5, pp. 304–307, 2006. View at Publisher · View at Google Scholar · View at Scopus
  3. C. Komninakis, C. Fragouli, A. H. Sayed, and R. D. Wesel, “Multi-input multi-output fading channel tracking and equalization using Kalman estimation,” IEEE Transactions on Signal Processing, vol. 50, no. 5, pp. 1065–1076, 2002. View at Publisher · View at Google Scholar · View at Scopus
  4. P. Wang, H. Li, and B. Himed, “A new parametric GLRT for multichannel adaptive signal detection,” IEEE Transactions on Signal Processing, vol. 58, no. 1, pp. 317–325, 2010. View at Publisher · View at Google Scholar · View at Scopus
  5. S. Marple, Digital Spectral Analysis with Applications, Prentice-Hall, Englewood Cliffs, NJ, USA, 1987.
  6. D. T. Pham and D. Q. Tong, “Maximum likelihood estimation for a multivariate autoregressive model,” IEEE Transactions on Signal Processing, vol. 42, no. 11, pp. 3061–3072, 1994. View at Publisher · View at Google Scholar · View at Scopus
  7. A. Schlogl, “A comparison of multivariate autoregressive estimators,” Signal Process, vol. 86, no. 9, pp. 2426–2429, 2006. View at Google Scholar
  8. A. Nehorai and P. Stoica, “Adaptive algorithms for constrained ARMA signals in the presence of noise,” IEEE Transactions on Acoustics, Speech, and Signal Processing, vol. 36, no. 8, pp. 1282–1291, 1988. View at Publisher · View at Google Scholar · View at Scopus
  9. W. X. Zheng, “Fast identification of autoregressive signals from noisy observations,” IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 52, no. 1, pp. 43–48, 2005. View at Publisher · View at Google Scholar · View at Scopus
  10. A. Mahmoudi and M. Karimi, “Estimation of the parameters of multichannel autoregressive signals from noisy observations,” Signal Processing, vol. 88, no. 11, pp. 2777–2783, 2008. View at Publisher · View at Google Scholar · View at Scopus
  11. W. X. Zheng, “Autoregressive parameter estimation from noisy data,” IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing, vol. 47, no. 1, pp. 71–75, 2000. View at Publisher · View at Google Scholar · View at Scopus
  12. X. M. Qu, J. Zhou, and Y. T. Luo, “A new noise-compensated estimation scheme for multichannel autoregressive signals from noisy observations,” Journal of Supercomputing, vol. 58, no. 1, pp. 34–49, 2011. View at Publisher · View at Google Scholar · View at Scopus
  13. J. Petitjean, E. Grivel, W. Bobillet, and P. Roussilhe, “Multichannel AR parameter estimation from noisy observations as an errors-in-variables issue,” Signal, Image and Video Processing, vol. 4, no. 2, pp. 209–220, 2010. View at Google Scholar
  14. A. Jamoos, E. Grivel, N. Shakarneh, and H. Abdel-Nour, “Dual optimal filters for parameter estimation of a multivariate autoregressive process from noisy observations,” IET Signal Processing, vol. 5, no. 5, pp. 471–479, 2011. View at Publisher · View at Google Scholar · View at Scopus
  15. D. Labarre, E. Grivel, Y. Berthoumieu, E. Todini, and M. Najim, “Consistent estimation of autoregressive parameters from noisy observations based on two interacting Kalman filters,” Signal Processing, vol. 86, no. 10, pp. 2863–2876, 2006. View at Publisher · View at Google Scholar · View at Scopus
  16. D. Labarre, E. Grivel, M. Najim, and N. Christov, “Dual H algorithms for signal processing-application to speech enhancement,” IEEE Transactions on Signal Processing, vol. 55, no. 11, pp. 5195–5208, 2007. View at Publisher · View at Google Scholar · View at Scopus
  17. W. Bobillet, R. Diversi, E. Grivel, R. Guidorzi, M. Najim, and U. Soverini, “Speech enhancement combining optimal smoothing and errors-in-variables identification of noisy AR processes,” IEEE Transactions on Signal Processing, vol. 55, no. 12, pp. 5564–5578, 2007. View at Publisher · View at Google Scholar · View at Scopus