Nonlinear RLS algorithm using variable forgetting factor in mixture noise

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Pages (from-to)3777-3780
Journal / PublicationICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume6
Publication statusPublished - 2001

Conference

Title2001 IEEE Interntional Conference on Acoustics, Speech, and Signal Processing
PlaceUnited States
CitySalt Lake, UT
Period7 - 11 May 2001

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.