Stochastic Event-Triggered Estimation With Smart Sensors Over Packet-Dropping Links

The event-triggered scheme (ETS) has been widely used for sensor data scheduling in cyber-physical systems. Existing literature on the design of ETSs for packet drops deals with the issue of non-Gaussianity of the a posteriori distribution in the system state. On the one hand, the Gaussian assumption only derives an approximate result, while on the other hand, exact results can be obtained by numerical integration but with excessive computational complexity. To this end, in this article, we propose a stochastic ETS based on acknowledgment information for remote state estimation with smart sensors and packet drops. The transmission decision is jointly driven by the holding time at the remote end and the accumulated innovative information. Then, we inductively derive the exact probability density function of the augmented innovative information vector by the Bayesian rule, which is used to obtain the explicit form of the estimation error covariance. These exact theoretical results mean that the design of scheduling parameter sequences no longer relies on experience. Finally, numerical simulations are provided to demonstrate that the empirical results agree with the theoretical results.