Statistical averaging and PARTAN-some alternatives to LMS and RLS

Author

Tao, K. Mike

Author_Institution

Integrated Systems Inc., Santa Clara, CA, USA

Volume

4

fYear

1992

fDate

23-26 Mar 1992

Firstpage

25

Abstract

Statistical averaging and PARTAN (parallel tangent) are shown as potential alternatives to the conventional least mean square (LMS) and recursive least squares (RLS) for adaptive filtering and signal processing. These new algorithms exhibit the RLS-like fast adaptation, and yet relate well to the popular LMS in conceptual simplicity and in implementation. Compared with the RLS, these new algorithms seem to be more robust in guarding against the signal bursting phenomenon. The computational complexity of these algorithms, in their direct forms, is less than that of the standard RLS but higher than the fast RLS and LMS

Keywords

adaptive filters; digital filters; filtering and prediction theory; signal processing; statistical analysis; PARTAN; adaptive filtering; computational complexity; parallel tangent; signal processing; statistical averaging; Adaptive filters; Adaptive signal processing; Computational complexity; Equations; Filtering algorithms; Least squares approximation; Resonance light scattering; Signal processing algorithms; Stochastic processes; Vectors;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech, and Signal Processing, 1992. ICASSP-92., 1992 IEEE International Conference on

Conference_Location

San Francisco, CA

ISSN

1520-6149

Print_ISBN

0-7803-0532-9

Type

conf

DOI

10.1109/ICASSP.1992.226420

Filename

226420