Prediction in LMS-type adaptive algorithms for smoothly time varying environments

IEEE Transactions on Signal Processing

Volumn 47, Issue 6, 1999, Pages 1735-1739

  Gazor, Saeed ^a,b  

^a ISFAHAN UNIVERSITY OF TECHNOLOGY   (Iran)

^b UNIVERSITY OF TORONTO   (Canada)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE ALGORITHMS; COMPUTATIONAL COMPLEXITY; COMPUTER SIMULATION; ITERATIVE METHODS; KALMAN FILTERING; LEAST SQUARES APPROXIMATIONS; MARKOV PROCESSES; MATHEMATICAL MODELS; PARAMETER ESTIMATION; VECTORS;

LEAST MEAN SQUARE; MARKOV NONSTATIONARY MODEL; SIGNAL SMOOTHING; TIME VARYING MODELS;

ADAPTIVE FILTERING;

EID: 0032687156     PISSN: 1053587X     EISSN: None     Source Type: Journal    
DOI: 10.1109/78.765152     Document Type: Article

References (8)

1. B. Widrow and Walach"On the statistical efficiency of the LMS algorithm with nonstationary inputs," IEEE Trans. Inform. Theory, Special Issue on Adaptive Filtering, vol. IT-30, Mar. 1984.
2. O. Macchi"Optimization of adaptive identification for time-varying filters," IEEE Trans. Automat. Contr.. vol. AC-31, pp. 283-287, 1986.
3. A. Benveniste"Design of one-step and multi-step adaptive algorithms for the tracking of time-varying systems," Rapports de Recherche, INRIA, IRISA, no. 340, Sept. 1984.
4. B. Farhang-BorojenyAdaptive Filters: Theory and Applications. London: U.K.: Wiley, 1998.
5. B. Farhang-Borojeny and S. Gazor"Performance of LMS-based adaptive filters in tracking a time-varying plant," IEEE Trans. Signal Processing, vol. 44, pp. 2868-2871, Nov. 1996.
6. W. Gersh and Kitagava"A smoothness priors-time-varying AR coefficient modeling of nonstationary covariance time series," in Proc. CDC, Las Vegas, NV, Dec. 1984.
7. S. HaykinAdaptive Filter Theory, 3rd ed. Englewood Cliffs, NJ: Prentice-Hall, 1996.
8. M. J. OmidiS. Gazor, P. G. Gulak, and S. Pasupathy, "Differential Kaiman filtering for tracking Rayleigh fading channels," in Proc. IEEE Workshop Signal Process. Syst., Oct. 1998, pp. 376-385.