Cubature Kalman filter Based on generalized minimum error entropy with fiducial point

TL;DR

This paper introduces a modified generalized minimum error entropy criterion with fiducial point to enhance the robustness of the cubature Kalman filter against non-Gaussian noise and outliers, demonstrating superior performance in simulations.

Contribution

A new GMEEFP-based cubature Kalman filter algorithm is proposed, improving robustness to impulsive disturbances compared to existing CKF methods.

Findings

Outperforms existing CKF algorithms under impulse noise

Demonstrates robustness to non-Gaussian disturbances

Effective in practical simulation scenarios

Abstract

In real applications, non-Gaussian distributions are frequently caused by outliers and impulsive disturbances, and these will impair the performance of the classical cubature Kalman filter (CKF) algorithm. In this letter, a modified generalized minimum error entropy criterion with fiducial point (GMEEFP) is studied to ensure that the error comes together to around zero, and a new CKF algorithm based on the GMEEFP criterion, called GMEEFP-CKF algorithm, is developed. To demonstrate the practicality of the GMEEFP-CKF algorithm, several simulations are performed, and it is demonstrated that the proposed GMEEFP-CKF algorithm outperforms the existing CKF algorithms with impulse noise.