An Array Recursive Least-Squares Algorithm With Generic Nonfading Regularization Matrix

Tsakiris, Manolis C.; Lopes, Cassio G.; Nascimento, Vitor H.

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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

An Array Recursive Least-Squares Algorithm With Generic Nonfading Regularization Matrix

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Author(s):	Tsakiris, Manolis C. ^[1] ; Lopes, Cassio G. ^[1] ; Nascimento, Vitor H. ^[1] Total Authors: 3
Affiliation:	^[1] Univ Sao Paulo, Dept Elect Syst, Escola Politecn, Sao Paulo - Brazil Total Affiliations: 1
Document type:	Journal article
Source:	IEEE SIGNAL PROCESSING LETTERS; v. 17, n. 12, p. 1001-1004, DEC 2010.
Web of Science Citations:	5
Abstract
We present a novel array RLS algorithm with forgetting factor that circumvents the problem of fading regularization, inherent to the standard exponentially-weighted RLS, by allowing for time-varying regularization matrices with generic structure. Simulations in finite precision show the algorithm's superiority as compared to alternative algorithms in the context of adaptive beamforming. (AU)

FAPESP's process:	09/06837-4 - Robust Affine Projection Algorithms
Grantee:	Manolis Tsakiris
Support Opportunities:	Scholarships in Brazil - Master

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