@article{HU2022, 
author = {Yumei HU and Xuezhi WANG and Quan PAN and Zhentao HU and Bill MORAN},
title = {Variational Bayesian Kalman filter using natural gradient},
year = {2022},
journal = {Chinese Journal of Aeronautics},
volume = {35},
number = {5},
pages = {1-10},
keywords = {Kullback-Leibler divergence, Natural gradient, Nonlinear Kalman filter, Target tracking, Variational Bayesian optimization},
url = {https://www.sciopen.com/article/10.1016/j.cja.2021.08.033},
doi = {10.1016/j.cja.2021.08.033},
abstract = {We propose a technique based on the natural gradient method for variational lower bound maximization for a variational Bayesian Kalman filter. The natural gradient approach is applied to the Kullback-Leibler divergence between the parameterized variational distribution and the posterior density of interest. Using a Gaussian assumption for the parametrized variational distribution, we obtain a closed-form iterative procedure for the Kullback-Leibler divergence minimization, producing estimates of the variational hyper-parameters of state estimation and the associated error covariance. Simulation results in both a Doppler radar tracking scenario and a bearing-only tracking scenario are presented, showing that the proposed natural gradient method outperforms existing methods which are based on other linearization techniques in terms of tracking accuracy.}
}