@Article{cmes.2021.019027,
AUTHOR = {Chen Xu, Yawen Mao, Hongtian Chen, Hongfeng Tao, Fei Liu},
TITLE = {Skew t Distribution-Based Nonlinear Filter with Asymmetric Measurement Noise Using Variational Bayesian Inference},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {131},
YEAR = {2022},
NUMBER = {1},
PAGES = {349--364},
URL = {http://www.techscience.com/CMES/v131n1/46640},
ISSN = {1526-1506},
ABSTRACT = {This paper is focused on the state estimation problem for nonlinear systems with unknown statistics of measurement noise. Based on the cubature Kalman filter, we propose a new nonlinear filtering algorithm that employs a
skew t distribution to characterize the asymmetry of the measurement noise. The system states and the statistics
of skew t noise distribution, including the shape matrix, the scale matrix, and the degree of freedom (DOF) are
estimated jointly by employing variational Bayesian (VB) inference. The proposed method is validated in a target
tracking example. Results of the simulation indicate that the proposed nonlinear filter can perform satisfactorily
in the presence of unknown statistics of measurement noise and outperform than the existing state-of-the-art
nonlinear filters.},
DOI = {10.32604/cmes.2021.019027}
}