Comparison of SPARLS and RLS algorithms for adaptive filtering

Author

Babadi, Behtash ; Kalouptsidis, Nicholas ; Tarokh, Vahid

Author_Institution

Sch. of Eng. & Appl. Sci., Harvard Univ., Cambridge, MA

fYear

2009

fDate

March 30 2009-April 1 2009

Firstpage

1

Lastpage

5

Abstract

In this paper, we overview the low complexity recursive L₁-Regularized Least Squares (SPARLS) algorithm proposed in [2], for the estimation of sparse signals in an adaptive filtering setting. The SPARLS algorithm is based on an Expectation-Maximization type algorithm adapted for online estimation. Simulation results for the estimation of multi-path wireless channels show that the SPARLS algorithm has significant improvement over the conventional widely-used Recursive Least Squares (RLS) algorithm, in terms of both mean squared error (MSE) and computational complexity.

Keywords

adaptive filters; computational complexity; expectation-maximisation algorithm; least mean squares methods; wireless channels; RLS algorithms; SPARLS algorithms; adaptive filtering; computational complexity; expectation-maximization type algorithm; low complexity recursive regularized least square algorithm; mean squared error algorithm; multipath wireless channels; online estimation; sparse signal estimation; Adaptive filters; Computational complexity; Convergence; Filtering algorithms; Least squares approximation; Least squares methods; Resonance light scattering; Signal processing; Signal processing algorithms; State estimation;

fLanguage

English

Publisher

ieee

Conference_Titel

Sarnoff Symposium, 2009. SARNOFF '09. IEEE

Conference_Location

Princeton, NJ

Print_ISBN

978-1-4244-3381-0

Electronic_ISBN

978-1-4244-3382-7

Type

conf

DOI

10.1109/SARNOF.2009.4850336

Filename

4850336