Nonlinear active noise control using diagonal-channel LMS and RLS bilinear filters

Author

Li Tan ; Jean Jiang

Author_Institution

Signal Process. & Instrum. Lab., Purdue Univ. North Central, West Lafayette, IN, USA

fYear

2014

fDate

3-6 Aug. 2014

Firstpage

789

Lastpage

792

Abstract

This paper proposes an adaptive bilinear filter with a diagonal-channel structure for nonlinear active noise control. Based on the diagonal-channel structure, the diagonal-channel bilinear filtered-X least mean square (DBFXLMS) and recursive least square (DBFXRLS) algorithms are derived. In order to reduce the computational load for the DBFXRLS algorithm, the DBFXRLS algorithm with a sequential channel update (DBFXRLS-SEQ) is proposed. Computational complexity for each algorithm is examined. Computer simulations demonstrate the control performance improvement using the proposed algorithms.

Keywords

active noise control; adaptive filters; computational complexity; least mean squares methods; nonlinear control systems; nonlinear filters; DBFXRLS algorithm; DBFXRLS-SEQ algorithm; RLS bilinear filters; adaptive bilinear filter; computational complexity; computational load reduction; computer simulations; diagonal-channel LMS structure; diagonal-channel bilinear filtered-X least mean square algorithm; nonlinear active noise control; recursive least square algorithms; sequential channel update; Adaptive filters; Filtering algorithms; Maximum likelihood detection; Noise; Nonlinear filters; Signal processing algorithms; Vectors; Active noise control (ANC); adaptive Volterra filter; adaptive bilinear filter; diagonal-channel structure; nonlinear adaptive filter;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems (MWSCAS), 2014 IEEE 57th International Midwest Symposium on

Conference_Location

College Station, TX

ISSN

1548-3746

Print_ISBN

978-1-4799-4134-6

Type

conf

DOI

10.1109/MWSCAS.2014.6908533

Filename

6908533