Robust FxLMS algorithms with improved convergence performance

This paper proposes two modifications of the filtered-x least mean squares (FxLMS) algorithm with improved convergence behavior albeit at the same computational cost of 2M operations per time step as the original FxLMS update. The paper further introduces a generalized FxLMS recursion and establishes that the various algorithms are all of filtered-error form. A choice of the stepsize parameter that guarantees faster convergence and conditions for robustness are also derived. Several simulation results are included to illustrate the discussions.


Published in:
IEEE Transactions on Speech and Audio Processing, 6, 1, 78-85
Year:
1998
Publisher:
IEEE
Laboratories:




 Record created 2017-12-19, last modified 2018-09-13


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