Improved ${\bi l}_{\bf 0}$l0-RLS adaptive filter

In this Letter, the authors present an improved sparse recursive least squares (RLS) algorithm, which employs a novel approximation of the $l_0$l0 norm of the filter coefficient vector for regularising the RLS cost function. The proposed algorithm achieves improved performance over existing algorith...

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Bibliographic Details
Published in:	Electronics letters Vol. 53; no. 25; pp. 1650 - 1651
Main Authors:	Das, B.K, Chakraborty, M
Format:	Journal Article
Language:	English
Published:	The Institution of Engineering and Technology 07.12.2017
Subjects:	adaptive filters filter coefficient vector improved l0‐RLS adaptive filter Information and communications l0 norm approximation least squares approximations numerical simulations RLS cost function sparse recursive least squares algorithm vectors sparse recursive least squares algorithm numerical simulations adaptive filters least squares approximations vectors RLS cost function filter coefficient vector improved l0-RLS adaptive filter l0 norm approximation
ISSN:	0013-5194, 1350-911X, 1350-911X
Online Access:	Get full text
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Summary:	In this Letter, the authors present an improved sparse recursive least squares (RLS) algorithm, which employs a novel approximation of the $l_0$l0 norm of the filter coefficient vector for regularising the RLS cost function. The proposed algorithm achieves improved performance over existing algorithms as demonstrated via numerical simulations.
ISSN:	0013-5194 1350-911X 1350-911X
DOI:	10.1049/el.2017.3441