Event-based State Estimation of Linear Dynamical Systems: Communication Rate Analysis

This work presents results on communication rate analysis for event-based state estimation schemes. An event-based estimator in the form of the Kalman filter with intermittent observations is first introduced, based on which time-varying upper and lower bounds on the expectation of the communication rate are developed. For stable systems, time-invariant upper and lower bounds are given. For sensors whose measurement values are scalars, the exact expression for the expectation of the communication rate is obtained. Numerical examples are presented to illustrate the results. It is shown that the developed results are rich enough to be generalized to recover existing results obtained for event-based minimum mean squared error estimates and estimates under more general event-triggering schemes.