To date, most linear and nonlinear Kalman filters (KFs) have been developed under the Gaussian assumption well-known minimum mean square error (MMSE) criterion. In order to improve robustness with respect impulsive (or heavy-tailed) non-Gaussian noises, maximum correntropy criterion (MCC) has recently used replace MMSE in developing several robust Kalman-type filters. deal more complicated nois...

In this paper, a kernel minimum error entropy (KMEE) based estimator is proposed for the estimation of multiple targets’ direction departure (DOD), arrival (DOA), and Doppler shift with input output radar in presence non-Gaussian clutter. Most existing approaches are on optimization complex cost function which often leads to sub-optimum solution. Therefore, accurate DOD, DOA shift, an efficient...

This paper presents a new performance enhancement method of information-theoretic learning (ITL) based blind equalizer algorithms for ISI communication channel environments with a mixture of AWGN and impulsive noise. The Gaussian kernel of Euclidian distance (ED) minimizing blind algorithm using a set of evenly generated symbols has the net effect of reducing the contribution of samples that ar...

Correntropy is a second order statistical measure in kernel space, which has been successfully applied in robust learning and signal processing. In this paper, we define a nonsecond order statistical measure in kernel space, called the kernel mean-p power error (KMPE), including the correntropic loss (C-Loss) as a special case. Some basic properties of KMPE are presented. In particular, we appl...

Stemming from information-theoretic learning, the correntropy criterion and its applications to machine learning tasks have been extensively studied explored. Its application regression problems leads robustness-enhanced paradigm: correntropy-based regression. Having drawn a great variety of successful real-world applications, theoretical properties also investigated recently in series studies ...