Multi-Sensor Centralized Fusion without Measurement Noise Covariance by Variational Bayesian Approximation

The work presented here solves the multi-sensor centralized fusion problem in the linear Gaussian model without the measurement noise variance. We generalize the variational Bayesian approximation based adaptive Kalman filter (VB_AKF) from the single sensor filtering to a multi-sensor fusion system, and propose two new centralized fusion algorithms, i.e., VB_AKF-based augmented centralized fusion algorithm and VB_AKF-based sequential centralized fusion algorithm, to deal with the case that the measurement noise variance is unknown. The simulation results show the effectiveness of the proposed algorithms.