Generalized Minimum Error Entropy Unscented Kalman Filter Based on Variational Bayesian

In this paper, a new adaptive outlier-robust filter is developed to address the nonlinear filtering problem with time-varying and unknown heavy-tailed measurement noises. The generalized minimum entropy criterion is employed to offer greater flexibility in noise adaptation, and the variational Bayesian (VB) technique is utilized to further infer the noise statistics online. The fixed-point iteration strategy is employed to address the coupling between the variational inference and the entropy criterion optimization. A target tracking example is conducted to verify the superiority of the developed algorithm. The results show that the robust nonlinear filtering algorithm developed using the GMEE criterion has strong noise suppression capability.