Reference : Convex Combination of Constraint Vectors for Set-membership Affine Projection Algorithms

	Document type :	Scientific congresses, symposiums and conference proceedings : Paper published in a book
	Discipline(s) :	Engineering, computing & technology : Electrical & electronics engineering
	Focus Areas :	Security, Reliability and Trust
	To cite this reference:	http://hdl.handle.net/10993/40373

Title :	Convex Combination of Constraint Vectors for Set-membership Affine Projection Algorithms
Language :	English
Author, co-author :	Nagashima Ferreira, Tadeu [UFF - Brazil]
	Alves Martins, Wallace [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > >]
	Lima, Markus [Federal University of Rio de Janeiro (UFRJ)]
	Diniz, Paulo [Federal University of Rio de Janeiro (UFRJ)]
Publication date :	May-2019
Main document title :	Proc. of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Pages :	4858-4862
Peer reviewed :	Yes
On invitation :	No
Audience :	International
Event name :	IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP-2019)
Event date :	12-17 May 2019
Abstract :	[en] Set-membership affine projection (SM-AP) adaptive filters have been increasingly employed in the context of online data-selective learning. A key aspect for their good performance in terms of both convergence speed and steady-state mean-squared error is the choice of the so-called constraint vector. Optimal constraint vectors were recently proposed relying on convex optimization tools, which might some- times lead to prohibitive computational burden. This paper proposes a convex combination of simpler constraint vectors whose performance approaches the optimal solution closely, utilizing much fewer computations. Some illustrative examples confirm that the sub-optimal solution follows the accomplishments of the optimal one.
Target :	Researchers ; Professionals ; Students
Permalink :	http://hdl.handle.net/10993/40373
Framework Programme / European Project :	H2020 ; 742648 - AGNOSTIC - Actively Enhanced Cognition based Framework for Design of Complex Systems

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