Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/94730
Type: Artigo de evento
Title: Impact Of Higher-order Statistics On Adaptive Algorithms For Blind Source Separation
Author: Cavalcante C.C.
Romano J.M.T.
Abstract: The paper is devoted to present an analysis of the impact of higher order statistics (HOS) in adaptive blind source separation criteria. Despite the well known fact that they are necessary to provide source separation in a general framework, their impact on the performance of adaptive solutions is a still open research field. The approach of probability density function (pdf) recovering is used. In order to verify the analysis, two constrained adaptive algorithms are investigated. Namely, the multiuser kurtosis algorithm (MUK) and the multiuser constrained fitting probability density function algorithm (MU-CFPA) are used due to the desired characteristics of different HOS involved in their design. Simulation results are carried out to basis our analysis. © 2004 IEEE.
Editor: 
Rights: fechado
Identifier DOI: 
Address: http://www.scopus.com/inward/record.url?eid=2-s2.0-21644450938&partnerID=40&md5=99e48afbb5e9e1b775b49793a3fe625c
Date Issue: 2004
Appears in Collections:Unicamp - Artigos e Outros Documentos

Files in This Item:
File Description SizeFormat 
2-s2.0-21644450938.pdf1.6 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.