Fast identification of autoregressive signals from noisy observations

@article{Zheng2005FastIO,
  title={Fast identification of autoregressive signals from noisy observations},
  author={Wei Xing Zheng},
  journal={IEEE Transactions on Circuits and Systems II: Express Briefs},
  year={2005},
  volume={52},
  pages={43-48}
}
  • Wei Xing Zheng
  • Published 2005 in
    IEEE Transactions on Circuits and Systems II…
The purpose of this brief is to derive, from the previously developed least-squares (LS) based method, a faster convergent approach to identification of noisy autoregressive (AR) stochastic signals. The feature of the new algorithm is that in its bias correction procedure, it makes use of more autocovariance samples to estimate the variance of the additive corrupting noise which determines the noise-induced bias in the LS estimates of the AR parameters. Since more accurate estimates of this… CONTINUE READING
Highly Cited
This paper has 38 citations. REVIEW CITATIONS

Citations

Publications citing this paper.
Showing 1-10 of 24 extracted citations

References

Publications referenced by this paper.
Showing 1-10 of 11 references

Autoregressive parameter estimation from noisy data

  • IEEE Trans. Circuits Syst. II, vol. 47, pp. 71–75…
  • 2000
Highly Influential
6 Excerpts

A least-squares based method for autoregressive signals in the presence of noise

  • IEEE Trans. Circuits Syst. II, vol. 46, pp. 81–85…
  • 1999
1 Excerpt

Introduction to Statistical Signal Processing with Applications

  • M. D. Srinath, P. K. Rajasekaran, R. Viswanathan
  • Englewood Cliffs, NJ: Prentice-Hall,
  • 1996
1 Excerpt

Tutorial on higher-order statistics (spectra) in signal processing and system theory: theoretical results and some applications

  • J. M. Mendel
  • Proc. IEEE, vol. 79, pp. 278–305, Mar. 1991.
  • 1991
2 Excerpts

Recursive parameter estimation of an autoregressive process disturbed by white noise

  • H. Sakai, M. Arase
  • Int. J. Control, vol. 30, no. 6, pp. 949–966…
  • 1979
1 Excerpt

Similar Papers

Loading similar papers…