Kalman-Like Filter for Event-Triggered Remote State Estimation Over an Additive Noise Channel

This letter studies the design of the Kalman-like filter for the event-triggered remote state estimation system over an additive noise channel. The triggering decisions are unavailable for the remote estimator. A Kalman-like filter in the minimum mean square sense is proposed by imposing a joint Gaussian assumption. In the proposed filter, the scaling matrix can adaptively change with the triggering decisions. The proposed filter is shown to be the Kalman filter in an extreme transmission case. The closed-form expression of the expected communication rate and the asymptotic bound of the expected error covariance are given. Numerical simulations demonstrate the effectiveness of the proposed filter with less available information.