International Journal of Adaptive Control and Signal Processing, ( ISI ), Volume (32), No (1), Year (2017-1) , Pages (83-97)

Title : ( Generalized maximum correntropy detector for non-Gaussian environments )

Authors: saeed hakimi ,

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Abstract

This paper addresses the problem of multiple-hypothesis detection. In many applications, assuming the Gaussian distribution for undesirable disturbances does not yield a sufficient model. On the other hand, under the non-Gaussian noise/interference assumption, the optimal detector will be impractically complex. Therewith, inspired by the optimal maximum likelihood detector, a suboptimal detector is designed. In particular, a novel detector based on the generalized correntropy, which adopts the generalized Gaussian density function as the kernel, is proposed. Simulations demonstrate that, in non-Gaussian noise models, the generalized correntropy detector significantly outperforms other commonly used detectors. The efficient and robust performance of the proposed detection method is illustrated in both light-tailed and heavy-tailed noise distributions.

Keywords

, detection, generalized correntropy, interference, non-Gaussian noise
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@article{paperid:1064559,
author = {Hakimi, Saeed},
title = {Generalized maximum correntropy detector for non-Gaussian environments},
journal = {International Journal of Adaptive Control and Signal Processing},
year = {2017},
volume = {32},
number = {1},
month = {January},
issn = {0890-6327},
pages = {83--97},
numpages = {14},
keywords = {detection; generalized correntropy; interference; non-Gaussian noise},
}

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%0 Journal Article
%T Generalized maximum correntropy detector for non-Gaussian environments
%A Hakimi, Saeed
%J International Journal of Adaptive Control and Signal Processing
%@ 0890-6327
%D 2017

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