@inproceedings{e6ed780150b141f9a20da9033405eaab,
title = "A Variational Bayesian Maximum Correntropy Cubature Kalman Filter with Adaptive Kernel Bandwidth",
abstract = "This paper focuses on the estimation problem in the presence of non-Gaussian measurement noise encountered in dynamic systems. The proposed solution is based on a variational Bayesian maximum correntropy cubature Kalman filter with adaptive kernel bandwidth that uses the Mahalanobis distance to adapt in real time the kernel bandwidth. The proposed filter is compared to some recent cubature Kalman filtering approaches, using the univariate nonstationary growth model benchmark. The obtained results demonstrate that the proposed method leads to the estimated values less affected by non-Gaussian measurement noises than other recent cubature Kalman-based filter.",
keywords = "maximum correntropy criterion, non-Gaussian noise, nonlinear filter, variational Bayesian",
author = "Zhenwei Li and Shuaijie Ouyang and Yongmei Cheng and Huibin Wang and Kezheng Chen",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023 ; Conference date: 14-11-2023 Through 17-11-2023",
year = "2023",
doi = "10.1109/ICSPCC59353.2023.10400217",
language = "英语",
series = "Proceedings of 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "Proceedings of 2023 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2023",
}