Stochastic Event-triggered Variational Bayesian Filtering

This paper proposes an event-triggered variational Bayesian filter for remote state estimation with unknown and time-varying noise covariances. After presetting multiple nominal process covariances initial measurement covariance, a method fixed-point iteration are utilized to jointly estimate the posterior vector under stochastic mechanism. The proposed algorithm ensures low communication loads excellent performances wide range of Finally, performance is demonstrated by tracking simulations vehicle.