An adaptive variational Bayesian filter for nonlinear multi-sensor systems with unknown noise statistics

An improved adaptive variational Bayesian cubature information fusion algorithm for nonlinear multi- sensor systems with uncertain noise statistics is proposed in this paper. Aiming to estimate uncertain process and measurement noise covariances in nonlinear systems, the variational Bayesian theory is com- bined with the inverse Wishart distribution. System states and uncertain noise covariances are jointly es- timated for nonlinear systems by means of cubature sampling, deriving the variational Bayesian cubature Kalman filter (VBCKF-QR). In addition, a variational Bayesian Cubature Information filter (VBCIF-QR) is proposed, and a distributed information feedback fusion algorithm is also derived for multi-sensor sys- tems with unknown noise statistics. Simulation results demonstrate that the proposed VBCKF-QR/VBCIF- QR outperform conventional cubature Kalman/information filters.