@inproceedings{90a0cdfaf676405fb28dcd03dbd257cf,
title = "Robust Variational Bayesian Filter for Systems with Skew t Noise",
abstract = "Considering the pulse interference, measurement outliers and artificial modeling errors, the non-Gaussian heavy-tailed (or skew) noise widely exists in the real environment. However, to data, little literature is related to the state estimation of the system where the process and measurement noises (PMNs) are both expressed as the skew t distribution (STD). To this end, given the hierarchical representation of the STD, a new robust Bayesian filter based on the variational Bayesian (VB) inference is presented to approximately estimate the unknown state via the collected measurements. And an example from the target tracking scenario is given to illustrate the validity of the designed Bayesian filter.",
keywords = "heavy-tailed noise, non-Gaussian noise, skew noise, variational Bayesian inference",
author = "Shuhui Li and Zhihong Deng and Ruxuan He and Feng Pan and Xiaoxue Feng and Ni Pu",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 Chinese Automation Congress, CAC 2020 ; Conference date: 06-11-2020 Through 08-11-2020",
year = "2020",
month = nov,
day = "6",
doi = "10.1109/CAC51589.2020.9327529",
language = "English",
series = "Proceedings - 2020 Chinese Automation Congress, CAC 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "6360--6365",
booktitle = "Proceedings - 2020 Chinese Automation Congress, CAC 2020",
address = "United States",
}