Distributed variational Bayesian adaptive filtering for randomly delayed measurements and unknown noise statistics in multi-sensor networked systems.

To deal with randomly delayed measurements and unknown noise statistics in multi-sensor networked systems, a distributed variational Bayesian adaptive cubature Kalman filtering algorithm (DVB_ACKF) is presented in this paper. To compensate for the effects induced by randomly delayed measurements, a refined measurement reorganization method is designed via a two-stage estimation strategy. Specifically, we first estimate the time delay step. The measurements are then rearranged based on the estimated results from the first stage. Then, a novel noise estimator with the sliding-window and variational Bayesian method is developed to estimate the mean and covariance of the unknown noise. The network topology attribute and measurement accuracy have been combined to eliminate the distributed fusion error. The tuning parameter and forgetting factor as well as their bounds are analyzed. Furthermore, numerical simulations are carried out to verify the superior performance of the developed algorithm. [ABSTRACT FROM AUTHOR]