[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.
Disciplines :
Electrical & electronics engineering
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)
External co-authors :
yes
Language :
English
Title :
Convex Combination of Constraint Vectors for Set-membership Affine Projection Algorithms
Publication date :
May 2019
Event name :
IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP-2019)
Event date :
12-17 May 2019
Audience :
International
Main work title :
Proc. of IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Pages :
4858-4862
Peer reviewed :
Peer reviewed
Focus Area :
Security, Reliability and Trust
European Projects :
H2020 - 742648 - AGNOSTIC - Actively Enhanced Cognition based Framework for Design of Complex Systems