Non-iterative Cauchy kernel-based maximum correntropy cubature Kalman filter for non-Gaussian systems

This article addresses the nonlinear state estimation problem where the conventional Gaussian assumption is completely relaxed. Here, the uncertainties in process and measurements are assumed non-Gaussian, such that the maximum correntropy criterion (MCC) is chosen to replace the conventional minimum mean square error criterion. Furthermore, the MCC is realized using Gaussian as well as Cauchy kernels by defining an appropriate cost function. Simulation results demonstrate the superior estimation accuracy of the developed estimators for two nonlinear estimation problems.