Stochastic event-triggered remote state estimation over Gaussian channels without knowing triggering decisions: A Bayesian inference approach

Abstract

This paper investigates the stochastic event-triggered remote state estimation problem over a Gaussian communication channel. The triggering decisions of the sensor determine the transmissions of measurements, and are unknown to the remote estimator due to the interference of channel noises. Based on a commonly-accepted Gaussian assumption, an approximate minimum mean squared error estimator with adaptive weights is derived by a Bayesian inference approach. The approximate estimator convexly combines the estimates for both transmission and no transmission cases, and the weights are adaptively updated according to the received data. Further, the proposed estimator behaves like the Kalman filtering with intermittent observations under two extreme situations. Finally, the a posteriori distribution of the estimation process is analyzed when the remote estimator knows the triggering decisions. Numerical results demonstrate that the performance of our estimator is comparable to those that know the triggering decisions, and also better than the detection-based estimator.