arallel Adaptation Algorithm for ecursive-Least-Squares Adaptive ilters in Nonstationary Environments

Abslruct- An accurate new expression €or the steady-state tracking performance of exponentially weighted recursive-leastsquares (IUS) adaptive filters in a random walk scenario is derived. This relation is then used to provide a detailed comparison between RLS performance and that of normalized least-meansquares adaptive filters. Further, a variable-forgetting-factor algorithm referred to as the parallel adaptation algorithm that approximately achieves the theoretical minimum mean-square&error performance in a random walk scenario is developed. Extensive simulation results are presented to support the present findings and demonstrate the improved performance of the proposed algorithm in a number of different applications.

[1]  B. Widrow,et al.  Stationary and nonstationary learning characteristics of the LMS adaptive filter , 1976, Proceedings of the IEEE.

[2]  Dirk T. M. Slock,et al.  On the convergence behavior of the LMS and the normalized LMS algorithms , 1993, IEEE Trans. Signal Process..

[3]  T. R. Fortescue,et al.  Implementation of self-tuning regulators with variable forgetting factors , 1981, Autom..

[4]  T. Kailath,et al.  Numerically stable fast transversal filters for recursive least squares adaptive filtering , 1991, IEEE Trans. Signal Process..

[5]  Lloyd J. Griffiths,et al.  A comparison of two fast linear predictors , 1981, ICASSP.

[6]  O. Macchi.  Optimization of adaptive identification for time-varying filters , 1984, The 23rd IEEE Conference on Decision and Control.

[7]  S. H. Ardalan,et al.  Fixed-point roundoff error analysis of the exponentially windowed RLS algorithm for time-varying systems , 1987, IEEE Trans. Acoust. Speech Signal Process..

[8]  Lennart Ljung,et al.  Adaptation and tracking in system identification - A survey , 1990, Autom..

[9]  S. Haykin,et al.  Adaptive Filter Theory , 1986 .

[10]  Boaz Porat.  Second-order equivalence of rectangular and exponential windows in least-squares estimation of Gaussian autoregressive processes , 1985, IEEE Trans. Acoust. Speech Signal Process..

[11]  P. Priouret,et al.  Performance analysis of the forgetting factor RLS algorithm , 1993 .

[12]  Fuyun Ling,et al.  Nonstationary learning characteristics of least squares adaptive estimation algorithms , 1984, ICASSP.

[13]  S. Douglas Peters.  Doubly adaptive filters for nonstationary applications , 1993 .

[14]  Subbarayan Pasupathy,et al.  Tracking improvements in fast RLS algorithms using a variable forgetting factor , 1988, IEEE Trans. Acoust. Speech Signal Process..

[15]  David D. Falconer,et al.  Tracking properties and steady-state performance of RLS adaptive filter algorithms , 1986, IEEE Trans. Acoust. Speech Signal Process..

[16]  Lennart Ljung,et al.  Application of Fast Kalman Estimation to Adaptive Equalization , 1978, IEEE Trans. Commun..

[17]  Albert Benveniste.  Design of adaptive algorithms for the tracking of time‐varying systems , 1987 .