Title :
Density parameter estimation for additive Cauchy-Gaussian mixture
Author :
Yuan Chen ; Kuruoglu, Ercan Engin ; Hing Cheung So ; Long-Ting Huang ; Wen-Qin Wang
Author_Institution :
Dept. of Electron. Eng., City Univ. of Hong Kong, Hong Kong, China
fDate :
June 29 2014-July 2 2014
Abstract :
In this paper, a mixture noise model, which is a sum of symmetric Cauchy and zero-mean Gaussian random variables in time domain, is studied. The Cauchy and Gaussian distributions are characterized by the unknown median γ and variance σ2, respectively. The probability density function (PDF) and characteristic function (CF) of the mixture are also investigated which are calculated by the convolution of the two PDFs, and product of the two CFs, respectively. Due to the complication of the resultant PDF, typical approaches such as maximum likelihood estimator may not be able to estimate γ and σ2 reliably. Based on the resultant CF, we propose to employ the fractional lower-order moment estimator for their computation. Simulation results show the mean square error performance of the proposed method and a comparison with the Cramér-Rao lower bound is also provided.
Keywords :
Gaussian distribution; Gaussian noise; convolution; maximum likelihood estimation; probability; random processes; CF; Cramér-Rao lower bound; Gaussian distributions; PDF; additive Cauchy-Gaussian mixture; characteristic function; convolution; density parameter estimation; lower-order moment estimator; maximum likelihood estimator; mixture noise model; probability density function; symmetric Cauchy random variables; zero-mean Gaussian random variables; Conferences; Maximum likelihood estimation; Mean square error methods; Noise; Probability density function; Random variables; Additive Cauchy-Gaussian; Cauchy distribution; Gaussian distribution; Voigt function; fractional lower-order moment;
Conference_Titel :
Statistical Signal Processing (SSP), 2014 IEEE Workshop on
Conference_Location :
Gold Coast, VIC
DOI :
10.1109/SSP.2014.6884609
