Nonlinear event-based state estimation using particle filtering approach

Abstract

An event-based state estimation for nonlinear systems under the impact of noise is considered, that may guarantee acceptable estimation performances and minimize the number of sent measurements from the sensor to a remote estimator. We design a Recursive Bayesian filter that calculate an approximated probability density functions for a given state estimation problem. For simplicity, the considered probability density functions are approximated using particle filtering approach.