Stochastic Event-Triggered Particle Filtering for State Estimation

In this paper, the problem of event-triggered (ET) state estimation is studied for nonlinear non-Gaussian systems. Particle filtering (PF) state estimation approach is developed for systems with stochastic ET measurements to overcome the computational problem in minimum mean square error (MMSE) estimators in which the posterior probability function is non-Gaussian due to ET measurement information. The proposed event triggered particle filtering (ETPF) not only solves the problem of non-Gaussianity but also can handle any functional nonlinearity in the system. It is proved that particles are weighted by the predicted event-triggering (ET) probability density function in the estimator side. The application of the proposed methodology to an interconnected four-tank system is also provided to illustrate and demonstrate the effectiveness of our proposed design methodology.