Event-based state estimation algorithm using Markov chain approximation

This paper presents an algorithm for event-based state estimation, where the evolution of the state is governed by a set of Stochastic Differential Equations (SDEs). From the event-based sampling, measurements are generated only when predefined events happen rather than at each regular sampling time. The state estimation problem is then formulated to compute the probability density of the state of a given system, with the sequence of noisy measurements obtained by the event-based sampling. In this research, a general framework for the event-based state estimation problem is developed and a numerical algorithm based on Markov chain approximation method is proposed.