On properties of information matrices of delta-operator based adaptive signal processing algorithms

IEEE Transactions on Signal Processing

Volumn 45, Issue 10, 1997, Pages 2454-2467

  Qiang Li, Member ^a   Fan, H ^b  

^a HUGHES NETWORK SYSTEMS   (United States)

^b 705 Engineering Research Center   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE ALGORITHMS; DIGITAL FILTERS; MATHEMATICAL MODELS; MATHEMATICAL OPERATORS; MATHEMATICAL TRANSFORMATIONS; MATRIX ALGEBRA; SAMPLING;

CONTINUOUS TIME SYSTEMS; DELTA OPERATOR BASED ALGORITHMS; INFORMATION MATRICES;

DIGITAL SIGNAL PROCESSING;

EID: 0031259002     PISSN: 1053587X     EISSN: None     Source Type: Journal    
DOI: 10.1109/78.640711     Document Type: Article

References (23)

1. S. M. Kay, Modem Spectral Estimation. Englewood Cliffs, NJ Prentice-Hall, 1988
2. A. V. Oppenheim and R. W. Schafer, Digital Signal Processing. Englewood Cliffs, NJ: Prentice-Hall, 1975.
3. S. T. AlexanderTransient weight misadjustment properties for th finite precision LMS algorithmIEEE Trans. Acoust., Speech, Signa Processing, vol. ASSP-35, 1250-1258, Sept. 1987.
4. C. Caraiscos and B. LiuA roundoff error analysis of the LMS adaptiv algorithmIEEE Trans. Acoust., Speech, Signal Processing, vol. ASSP 32, 34-11, Feb. 1984.
5. H. FanA structural view of asymptotic convergence speed of adaptiv IIR filtering algorithms-Part I: Infinite precision implementation, IEEE Trans. Signal Processing, vol. 41, 1493-1517, Apr. 1993
6. also Part II: Finite precision implementation IEEE Trans. Signa Processing, vol. 45, 1458-1472, June 1997.
7. R. H. Middleton and G. C. Goodwin, Digital Control and Estimation- Unified Approach. Englewood Cliffs, NJ: Prentice-Hall, 1990.
8. R. Vijayan, H. V. Poor, J. B. Moore, and G. C. GoodwinA Levinson type algorithm for modeling fast-sampled dataIEEE Trans. Automat Contr., vol. AC-36, 314-321, Mar. 1991.
9. H. Fan and X. LiuDelta Levinson and Schur type RLS algorithm for adaptive signal processingIEEE Trans. Signal Processing, vol. 42 1629-1639, July 1994.
10. G. C. Goodwin, R. H. Middleton, and H. V. PoorHigh-speed digita signal processing and controlProc. IEEE, vol. 80, 240-259, Feb 1992.
11. F. JabbariLattice filters for RLS estimation of a delta operator-base modelIEEE Trans. Automat. Contr., vol. 36, 869-875, July 1991.
12. H. Fan and Q. LiA e-operator recursive gradient algorithm fo adaptive signal processingProc. Intl. Conf. Acoust., Speech, Signa Processing, Minneapolis, MN, vol. Ill, Apr. 1993, 492-4195.
13. G. CybenkoThe numerical stability of the Levinson-Durbin algorith for Toeplitz systems of equationsSIAM J. Sci. Statist. Comput., vol 1, no. 3, 303-319, Sept. 1980.
14. R. VijayanDifference-operator based models in statistical signa processing Ph.D. dissertation, Univ. Illinois, Urbana, 1991.
15. L. Ljung and T. Söderström, Theory and Practice of Recursive Identification. Cambridge, MA: MIT Press, 1983.
16. G. Li and M. Gevers, Parameterizations in Control, Estimation and Filtering Problems. New York: Springer-Verlag, 1993
17. Koltracht and P. LancasterCondition numbers of Toeplitz and block Toeplitz matricesOperator Theory: Advances Appl., vol. 18, 271-300, 1986
18. T. Söderström, H. Fan, S. Bigi, and B. CarlssonWhen is a least-squares fit feasible for modeling continuous-time autoregressive processes from discrete-time data? Tech. Rep. UPTEC95003R, Dept. Technol., Uppsala Univ., Uppsala, Sweden, Jan. 1995
19. Q. Li, H. Fan, and E. KarlssonA delta MYWE algorithm for parameter estimation of noisy AR processesIEEE Trans. Signal Processing, vol. 44, 1300-1303, May 1996
20. R. Bellman, Introduction to Matrix Analysis. York, PA: McGraw-Hill, 1960
21. T. Kailath, Linear Systems. Englewood Cliffs, NJ: Prentice-Hall, 1980
22. A. Feuer and R. MiddletonConditioning of LMS algorithms with fast samplingIEEE Trans. Signal Processing, vol. 43, 1978-1981, Aug. 1995
23. Q. LiDifference operators in adaptive signal processing Ph.D dissertation, Univ. Cincinnati, Cincinnati, OH, 1995.