Chen Xu1, Yawen Mao2, Hongtian Chen3,*, Hongfeng Tao1, Fei Liu1
CMES-Computer Modeling in Engineering & Sciences, Vol.131, No.1, pp. 349-364, 2022, DOI:10.32604/cmes.2021.019027
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… More >
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