Articolo in rivista, 2015, ENG, 10.1016/j.sigpro.2014.07.028
Chen J. Y.; Kuruoglu E.E.; So H.C.
Department of Electronic Engineering, City University of Hong Kong, Hong Kong, Hong Kong; CNR-ISTI, Pisa, Italy; Department of Electronic Engineering, City University of Hong Kong, Hong Kong, Hong Kong
We study the estimation problem of linear regression in the presence of a new impulsive noise model, which is a sum of Cauchy and Gaussian random variables in time domain. The probability density function (PDF) of this mixture noise, referred to as the Voigt profile, is derived from the convolution of the Cauchy and Gaussian PDFs. To determine the linear regression parameters, the maximum likelihood estimator (MLE) is developed first. Since the Voigt profile suffers from a complicated analytical form, an M-estimator with the pseudo-Voigt function is also derived. In our algorithm development, both scenarios of known and unknown density parameters are considered. For the latter case, we estimate the density parameters by utilizing the empirical characteristic function prior to applying the MLE. Simulation results show that the performance of both proposed methods can attain the Cramér-Rao lower bound. © 2014 Elsevier B.V.
Signal processing (Print) 106 , pp. 312–318
Cauchy distribution, Gaussian distribution, Impulsive noise
ISTI – Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"
ID: 295344
Year: 2015
Type: Articolo in rivista
Creation: 2015-01-28 13:39:24.000
Last update: 2021-04-08 00:35:01.000
CNR authors
External links
OAI-PMH: Dublin Core
OAI-PMH: Mods
OAI-PMH: RDF
DOI: 10.1016/j.sigpro.2014.07.028
URL: https://www.sciencedirect.com/science/article/abs/pii/S0165168414003624?via%3Dihub
External IDs
CNR OAI-PMH: oai:it.cnr:prodotti:295344
DOI: 10.1016/j.sigpro.2014.07.028
Scopus: 2-s2.0-84907342611
ISI Web of Science (WOS): 000344210200030