Recursive inverse adaptive filtering algorithm

Author

Ahmad, Mohammad Shukri ; Kukrer, Osman ; Hocanin, Aykut

fYear

2009

fDate

2-4 Sept. 2009

Firstpage

1

Lastpage

3

Abstract

In this paper, a new FIR adaptive filtering algorithm is introduced. This algorithm is based on the Quasi-Newton (QN) optimization algorithm. The approach uses a variable step-size in the coefficient update equation that leads to an improved performance. The simulation results show that the algorithm has very similar performance to the robust recursive least squares algorithm (RRLS) while performing better than the transform domain LMS with Variable Step-Size (TDVSS) in stationary environments. The algorithm is tested in additive white Gaussian noise (AWGN) and Correlated Noise environments.

Keywords

AWGN; FIR filters; adaptive signal processing; correlation methods; recursive filters; Quasi-Newton optimization; additive white Gaussian noise; coefficient update equation; correlated noise environments; recursive inverse adaptive filtering algorithm; robust recursive least squares algorithm; transform domain LMS; variable step-size; AWGN; Adaptive filters; Additive white noise; Equations; Filtering algorithms; Finite impulse response filter; Least squares approximation; Least squares methods; Noise robustness; Transforms; Adaptive Filters; RRLS; Recursive Inverse; TDVSS;

fLanguage

English

Publisher

ieee

Conference_Titel

Soft Computing, Computing with Words and Perceptions in System Analysis, Decision and Control, 2009. ICSCCW 2009. Fifth International Conference on

Conference_Location

Famagusta

Print_ISBN

978-1-4244-3429-9

Electronic_ISBN

978-1-4244-3428-2

Type

conf

DOI

10.1109/ICSCCW.2009.5379461

Filename

5379461