A noise-compensated estimation scheme for AR processes

Proceedings of the 44th IEEE Conference on Decision and Control, and the European Control Conference, CDC-ECC '05

Volumn 2005, Issue , 2005, Pages 4146-4151

  Diversi, Roberto ^a   Guidorzi, Roberto ^a   Soverini, Umberto ^a  

^a NONE

Author keywords

[No Author keywords available]

Indexed keywords

COMPUTER SIMULATION; MATRIX ALGEBRA; PARAMETER ESTIMATION; PROBLEM SOLVING; REGRESSION ANALYSIS;

AUTOREGRESSIVE MODELS; FRISCH SCHEME; NOISE COMPENSATED ESTIMATION; YULE WALKER EQUATIONS;

ACOUSTIC NOISE MEASUREMENT;

EID: 33847188491     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/CDC.2005.1582811     Document Type: Conference Paper

References (19)

1. S. M. Kay, The effects of noise on the autoregressive spectral estimator, IEEE Transactions on Acoustics, Speech and Signal Processing, vol. 27, 1979, pp. 478-485.
2. S. M. Kay, Modern Spectral Estimation, Prentice-Hall, Englewood Cliffs, New Jersey, 1988.
3. 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, 1988, pp. 1282-1291.
4. M. Pagano, Estimation of models of autoregressive signal plus white noise, The Annals of Statistics, vol. 2, 1974, pp. 99-108.
5. Y. T. Chan and R. P. Langford, Spectral estimation via the high-order Yule-Walker equations, IEEE Transactions on Acoustics, Speech and Signal Processing, vol. 30, 1982, pp. 689-698.
6. J. Cadzow, Spectral estimation: an overdetermined rational model equation approach, Proceedings of the IEEE, vol. 70, 1982, pp. 907-939.
7. S. M. Kay, Noise compensation for autoregressive spectral estimates, IEEE Transactions on Acoustics, Speech and Signal Processing, vol. 28, 1980, pp. 292-303.
8. T. Sen Lee, Large sample identification and spectral estimation of noisy multivariate autoregressive processes, IEEE Transactions on Acoustics, Speech and Signal Processing, vol. 31, 1983, pp. 76-82.
9. K. K. Paliwal, A noise-compensated long correlation matching method for AR spectral estimation of noisy signals, Signal Processing, vol. 15, 1988, pp. 437-440.
10. C. E. Davila, A subspace approach to estimation of autoregressive parameters from noisy measurements, IEEE Transactions on Signal Processing, vol. 46, 1998, pp. 531-534.
11. H. Sakai and M. Arase, Recursive parameter estimation of an autoregressive process disturbed by white noise, International Journal of Control, vol. 30, 1979, pp. 949-966.
12. W. X. Zheng, A least-squares based method for autoregressive signals in the presence of noise, IEEE Transactions on Circuits and Systems-II, vol. 46, 1999, pp. 81-85.
13. L.-J. Jia, S. Kanae, Z.-J. Yang and K. Wada, On bias compensation estimation for noisy AR process, Proceedings of the 42nd IEEE Conference on Decision and Control, Maui, Hawaii, USA, 2003, pp. 405-410.
14. W. X. Zheng, Fast identification of autoregressive signals from noisy observations, IEEE Transactions on Circuits and Systems-II, vol. 52, 2005, pp. 43-48.
15. R. Diversi, U. Soverini and R. Guidorzi, A new estimation approach for AR models in presence of noise, Preprints of the 16th IFAC World Congress, Prague, Czech Republic, 2005.
16. S. Beghelli, R. Guidorzi and U. Soverini, The Frisch scheme in dynamic system identification, Automatica, vol. 26, 1990, pp. 171-176.
17. R. Guidorzi and M. Pierantoni, A new parametrization of Frisch scheme solutions, Proceedings of the XII International Conference on Systems Science, Wroclaw, Poland, 1995, pp. 114-120.
18. C. E. Davila, On the noise-compensated Yule-Walker equations, IEEE Transactions on Signal Processing, vol. 49, 2001, pp. 1119-1121.
19. T. Söderström and P. Stoica, System Identification, Prentice-Hall, Cambridge, 1989.