Sparse adaptive filters - An overview and some new results

Author

Das, Rajib Lochan ; Chakraborty, Mrityunjoy

Author_Institution

Dept. of Electron. & Electr., Indian Inst. of Technol., Kharagpur, India

fYear

2012

fDate

20-23 May 2012

Firstpage

2745

Lastpage

2748

Abstract

In this paper, we provide an overview of the major developments in the area of sparse adaptive filters, starting from the celebrated works on PNLMS algorithm and its several variants to more recent approaches that use compressed sensing framework, more specifically, LASSO and basis pursuit or matching pursuit, to develop sparse adaptive algorithms with improved mean square error and tracking properties. Subsequently, we also present a new approach to identify sparse systems with time varying sparseness, for which a novel scheme of cooperative learning involving a PNLMS and a NLMS based adaptive filters is developed.

Keywords

adaptive filters; compressed sensing; iterative methods; learning (artificial intelligence); least mean squares methods; LASSO; PNLMS algorithm; basis pursuit; compressed sensing; cooperative learning; least absolute shrinkage and selection operator; matching pursuit; mean square error; proportionate normalized least mean square; sparse adaptive algorithms; sparse adaptive filter; Convergence; Filtering algorithms; Information filters; Least squares approximation; Matching pursuit algorithms; Signal processing; Adaptive Convex Combination; Basis Pursuit; Compressed Sensing; Cooperative Learning; LASSO; Matching Pursuit; PNLMS Algorithm; Sparse Adaptive Filter;

fLanguage

English

Publisher

ieee

Conference_Titel

Circuits and Systems (ISCAS), 2012 IEEE International Symposium on

Conference_Location

Seoul

ISSN

0271-4302

Print_ISBN

978-1-4673-0218-0

Type

conf

DOI

10.1109/ISCAS.2012.6271877

Filename

6271877