Partial update EDS algorithms for adaptive filtering

Author

Xie, Bei ; Bose, Tamal

Author_Institution

Bradley Dept. of Electr. & Comput. Eng., Virginia Polytech. Inst. & State Univ., Blacksburg, VA, USA

fYear

2010

fDate

14-19 March 2010

Firstpage

3750

Lastpage

3753

Abstract

In practice, computational complexity is an important consideration of an adaptive signal processing system. A well-known approach to controlling computational complexity is applying partial update (PU) adaptive filters. In this paper, a partial update Euclidean Direction Search (EDS) algorithm is employed. The theoretical analyses of mean and mean-square performance are presented. The simulation results of different PU EDS are shown.

Keywords

adaptive filters; adaptive signal processing; computational complexity; mean square error methods; Euclidean direction search algorithm; adaptive filtering; adaptive signal processing system; computational complexity; mean-square performance; partial update EDS algorithms; Adaptive filters; Adaptive signal processing; Computational complexity; Computational efficiency; Computational modeling; Convergence; Filtering algorithms; Performance analysis; Resonance light scattering; Signal processing algorithms; EDS; partial updates;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics Speech and Signal Processing (ICASSP), 2010 IEEE International Conference on

Conference_Location

Dallas, TX

ISSN

1520-6149

Print_ISBN

978-1-4244-4295-9

Electronic_ISBN

1520-6149

Type

conf

DOI

10.1109/ICASSP.2010.5495857

Filename

5495857