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