Nonlinear RLS algorithm using variable forgetting factor in mixture noise

S. H. LEUNG, Chi Fuk Henry SO

Research output: Chapter in Book/Report/Conference proceedingChapters

10 Citations (Scopus)

Abstract

In impulsive noise environment, most learning algorithms are encountered difficulty in distinguishing the nature of large error signal, whether caused by the impulse noise or model error. Consequently, they suffer from large misadjustment or otherwise slow convergence. A new nonlinear RLS (VFF-NRLS) adaptive algorithm with variable forgetting factor for FIR filter is introduced. In this algorithm, the autocorrelations of non-zero lags, which is insensitive to white noise, is used to control forgetting factor of the nonlinear RLS. This scheme makes the algorithm have fast tracking capability and small misadjustment. By experimental results, it is shown that the new algorithm can outperform other RLS algorithms. Copyright © 2001 by The Institute of Electrical and Electronics Engineers, Inc. All rights reserved.

Original languageEnglish
Title of host publicationProceedings of 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing
Place of PublicationUSA
PublisherIEEE
Pages3777-3780
VolumeVI
ISBN (Print)0780370414
DOIs
Publication statusPublished - 2001

Citation

Leung, S. H., & So, C. F. (2001). Nonlinear RLS algorithm using variable forgetting factor in mixture noise. In Proceedings of 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing (Vol. IV, pp. 3777-3780). USA: IEEE.

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