Optimum linear regression in additive Cauchy-Gaussian noise

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Detail(s)

Original languageEnglish
Pages (from-to)312-318
Journal / PublicationSignal Processing
Volume106
Publication statusPublished - Jan 2015

Abstract

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.

Research Area(s)

  • Cauchy distribution, Gaussian distribution, Impulsive noise, M-estimator, Maximum likelihood estimator, Mixture noise, Pseudo-Voigt function, Voigt profile

Citation Format(s)

Optimum linear regression in additive Cauchy-Gaussian noise. / Chen, Yuan; Kuruoglu, Ercan Engin; So, Hing Cheung.

In: Signal Processing, Vol. 106, 01.2015, p. 312-318.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review