Title :
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
fDate :
March 30 2009-April 1 2009
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;
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
DOI :
10.1109/SARNOF.2009.4850336
