Sparse adaptive filtering by iterative hard thresholding

Author

Das, Rajib Lochan ; Chakraborty, Manali

Author_Institution

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

fYear

2013

fDate

Oct. 29 2013-Nov. 1 2013

Firstpage

1

Lastpage

6

Abstract

In this paper, we present a new algorithm for sparse adaptive filtering, drawing from the ideas of a greedy compressed sensing recovery technique called the iterative hard thresholding (IHT) and the concepts of affine projection. While usage of affine projection makes it robust against colored input, the use of IHT provides a remarkable improvement in convergence speed over the existing sparse adaptive algorithms. Further, the gains in performance are achieved with very little increase in computational complexity.

Keywords

adaptive filters; compressed sensing; computational complexity; convergence of numerical methods; filtering theory; greedy algorithms; iterative methods; signal representation; signal sampling; IHT; affine projection; colored input; compressive sampling; computational complexity; convergence speed; greedy compressed sensing recovery technique; iterative hard thresholding; signal represention; sparse adaptive filtering; subNyquist sampling rate; Adaptive algorithms; Compressed sensing; Convergence; Indexes; Matching pursuit algorithms; Steady-state; Vectors; Affine Projection; Compressed Sensing; Iterative Hard Thresholding; PNLMS; Sparse Adaptive Filter;

fLanguage

English

Publisher

ieee

Conference_Titel

Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2013 Asia-Pacific

Conference_Location

Kaohsiung

Type

conf

DOI

10.1109/APSIPA.2013.6694326

Filename

6694326