Cauchy kernel minimum error entropy centralized fusion filter

Abstract

With the help of Information Theory Learning (ITL) theory, a kind of increasingly popular non-Gaussian filtering method has been designed in the sense of the maximum correntropy (MC) criterion or minimum error entropy (MEE) criterion. In MC or MEE criterion, Gaussian kernel function is usually chosen as the kernel function. Compared to the Gaussian kernel function, the Cauchy kernel function has a wider range of action and is insensitive to the kernel parameters. In this paper, a new Cauchy kernel MEE criterion is defined. In the sense of the new defined criterion, two kinds of non-Gaussian centralized fusion filtering algorithms are proposed with the help of fixed-point theory, which are Cauchy kernel minimum error entropy parallel centralized fusion filter and Cauchy kernel minimum error entropy sequential centralized fusion filter. The propagation equation is used to calculate the prior estimates of the state and covariance, and then the fixed-point iteration is used to calculate the posterior estimates of the state and covariance. The convergence of the iteration process included in the two proposed methods is analyzed by using Banach fixed-point theorem. The final two experimental simulations verify the superior performance of the two proposed fusion filtering algorithms.