Particle Filters for Set-membership State Estimation

This paper proposes a new way of using particle filters for set-membership state estimation problems. For nonlinear state estimation problems, stochastic particle filters have been proposed which maintain a large number of solution candidates by using Monte-Carlo simulation. Set-membership approach for state estimation is an alternative method that is, in certain situations, more suitable than stochastic models. However, parametric modeling of the set-membership description (e.g. ellipsoidal approximation) is not easy even for linear models, and it often yields sets that are much larger than true uncertainty sets. We show that particle filters that utilize Monte-Carlo simulation are more suitable for set-membership approach for nonlinear models