Catalogo Articoli (Spogli Riviste)

OPAC HELP

Titolo:
BEACON: An adaptive set-membership filtering technique with sparse updates
Autore:
Nagaraj, S; Gollamudi, S; Kapoor, S; Huang, YF;
Indirizzi:
Univ Notre Dame, Dept Elect Engn, Lab Image & Signal Anal, Notre Dame, IN 46556 USA Univ Notre Dame Notre Dame IN USA 46556 al Anal, Notre Dame, IN 46556 USA NEC USA Inc, Princeton, NJ 08840 USA NEC USA Inc Princeton NJ USA 08840NEC USA Inc, Princeton, NJ 08840 USA
Titolo Testata:
IEEE TRANSACTIONS ON SIGNAL PROCESSING
fascicolo: 11, volume: 47, anno: 1999,
pagine: 2928 - 2941
SICI:
1053-587X(199911)47:11<2928:BAASFT>2.0.ZU;2-L
Fonte:
ISI
Lingua:
ENG
Soggetto:
BOUNDED NOISE; IDENTIFICATION; SYSTEMS;
Tipo documento:
Article
Natura:
Periodico
Settore Disciplinare:
Engineering, Computing & Technology
--discip_EC--
Citazioni:
24
Recensione:
Indirizzi per estratti:
Indirizzo: Nagaraj, S Univ Notre Dame, Dept Elect Engn, Lab Image & Signal Anal, Notre Dame, IN 46556 USA Univ Notre Dame Notre Dame IN USA 46556 tre Dame, IN 46556 USA
Citazione:
S. Nagaraj et al., "BEACON: An adaptive set-membership filtering technique with sparse updates", IEEE SIGNAL, 47(11), 1999, pp. 2928-2941

Abstract

This paper deals with adaptive solutions to the so-called set-membership filtering (SMF) problem. The SMF methodology involves designing filters by imposing a deterministic con straint on the output error sequence. A set-membership decision feedback equalizer (SM-DFE) for equalization of a communications channel is derived, and connections with the minimum mean square error (MMSE) DFE are established. Further, an adaptive solution to the generalSMF problem via a novel optimal bounding ellipsoid (OBE) algorithm called BEACON is presented, This algorithm features sparse updating, wherein it uses about 5-10% of the data to update the parameter estimates without any loss in mean-squared error performance, in comparison with the conventional recursive least-squares (RLS) algorithm, It is shown that the BEACON algorithm can also be derived as a solution to a certain constrained least-squaresproblem. Simulation results are presented for various adaptive signal processing examples, including estimation of a real communication channel, Further, it is shown that the algorithm can accurately track fast time variations in a nonstationary environment. This improvement is a result of incorporating an explicit test to check if an update is needed at every time instant as well as an optimal data-dependent assignment to the updating weights whenever an update is required.

ASDD Area Sistemi Dipartimentali e Documentali, Università di Bologna, Catalogo delle riviste ed altri periodici
Documento generato il 05/07/20 alle ore 06:28:08