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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.)

RLS Adaptive Filter With Inequality Constraints

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Author(s):
Nascimento, Vitor H. [1] ; Zakharov, Yuriy V. [2]
Total Authors: 2
Affiliation:
[1] Univ Sao Paulo, Dept Elect Syst Engn, BR-05508970 Sao Paulo - Brazil
[2] Univ York, Dept Elect, York YO10 5DD, N Yorkshire - England
Total Affiliations: 2
Document type: Journal article
Source: IEEE SIGNAL PROCESSING LETTERS; v. 23, n. 5 MAY 2016.
Web of Science Citations: 7
Abstract

In practical implementations of estimation algorithms, designers usually have information about the range in which the unknown variables must lie either due to physical constraints (such as power always being non-negative) or due to hardware constraints (such as in implementations using fixedpoint arithmetic). In this letter, we propose a fast (i.e., whose complexity grows linearly with the filter length) version of the dichotomous coordinate descent recursive least-squares (RLS) adaptive filter which can incorporate constraints on the variables. The constraints can be in the form of lower and upper bounds on each entry of the filter, or norm bounds. We compare the proposed algorithm with the recently proposed normalized non-negative least-mean-squares (N-NLMS) and projected-gradient normalized LMS (PG-NLMS) filters, which also include inequality constraints in the variables. (AU)

FAPESP's process: 14/50765-6 - Knowledge-aided signal processing: theory, algorithms, implementation and applications
Grantee:Vitor Heloiz Nascimento
Support Opportunities: Regular Research Grants
FAPESP's process: 14/04256-2 - Low-cost algorithms for parameter estimation
Grantee:Vitor Heloiz Nascimento
Support Opportunities: Regular Research Grants