Title :
Mean weight behavior of the Filtered-X LMS algorithm
Author :
Tobias, O.J. ; Bermudez, J.C.M. ; Bershad, N.J. ; Seara, R.
Author_Institution :
Dept. of Electr. Eng., Univ. Fed. de Santa Catarina, Florianapolis, Brazil
Abstract :
This paper presents a stochastic analysis of the Filtered-X LMS algorithm. The mean weight vector recursion is derived for slow adaptation and for a white reference signal without use of independence theory. The Wiener solution is determined explicitly as a function of the input statistics and the impulse responses of the primary and secondary signal paths. It is shown that the steady-state mean weights for the Filtered-X LMS algorithm converge to the Wiener solution only if the estimate of the secondary path is without error. Monte Carlo simulations show excellent agreement with the behavior predicted by the theoretical model
Keywords :
active noise control; adaptive filters; adaptive signal processing; convergence of numerical methods; filtering theory; least mean squares methods; statistical analysis; transient response; Filtered-X LMS algorithm; Monte Carlo simulations; Wiener solution; acoustic noise control; active noise control; adaptive algorithm; convergence; impulse response; input statistics; mean weight behavior; mean weight vector recursion; primary signal path; secondary signal path; slow adaptation; steady-state mean weights; stochastic analysis; vibration control; white reference signal; Acoustic noise; Active noise reduction; Adaptive algorithm; Algorithm design and analysis; Least squares approximation; Statistics; Steady-state; Stochastic processes; Vibration control; Working environment noise;
Conference_Titel :
Acoustics, Speech and Signal Processing, 1998. Proceedings of the 1998 IEEE International Conference on
Print_ISBN :
0-7803-4428-6
DOI :
10.1109/ICASSP.1998.679637
