Generalized minimum error entropy Kalman filter for non-Gaussian noise

Jiacheng He; Gang Wang; Huijun Yu; JunMing Liu; Bei Peng

doi:10.1016/j.isatra.2022.10.040

Generalized minimum error entropy Kalman filter for non-Gaussian noise

ISA Trans. 2023 May:136:663-675. doi: 10.1016/j.isatra.2022.10.040. Epub 2022 Nov 10.

Authors

Jiacheng He¹, Gang Wang², Huijun Yu¹, JunMing Liu¹, Bei Peng³

Affiliations

¹ School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, PR China.
² School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, PR China.
³ School of Mechanical and Electrical Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, PR China. Electronic address: beipeng@uestc.edu.cn.

PMID: 36443119
DOI: 10.1016/j.isatra.2022.10.040

Abstract

Error entropy is a well-known learning criterion in information theoretic learning (ITL), and it has already been applied to a wide range of fields. However, the shape of error entropy cannot be changed freely since its kernel function is the Gaussian kernel function, which causes the error entropy-based algorithm to handle only some specific kinds of noises. Benefiting from the property that the generalized Gaussian kernel function is free to adjust its shape, a novel Kalman-type filter algorithm based on the generalized minimum error entropy (GMEEKF) criterion is derived. Moreover, the mean error behavior, mean square error behavior, and computational complexity of the GMEEKF algorithm are analyzed. Finally, several simulations and experiments are performed to demonstrate the performance of the GMEEKF algorithm in comparison with the existing Kalman-type filter algorithms.

Keywords: Error entropy; Generalized Gaussian kernel function; Generalized minimum error entropy; Kalman filter.