Gaussianity-Preserving Event-Based State Estimation with an FIR-Based Stochastic Trigger

With modern communication technology, sensors, estimators, and controllers can be pushed apart to distribute intelligence over wide distances. Instead of congesting channels by periodic data transmissions, smart sensors can decide on their own whether data are worth transmitting. This letter studies event-based transmissions from sensor to estimator. The sensor-side event trigger conveys usable information even if no transmission is triggered. In the absence of data, such implicit information can still be exploited by the remote Kalman filter. For this purpose, an easy-to-implement triggering mechanism is proposed based on a finite impulse response prediction that is compared against a stochastic decision variable. By the aid of the stochastic event trigger, the implicit information retains a Gaussian representation and can easily be processed by the Kalman filter. The parameters for the stochastic trigger are retrieved from the finite impulse response filter, which contributes to reducing the communication rate significantly, as shown in simulations.