Truncated unscented particle filter for dealing with non-linear and inequality constraints

Abstract

This paper presents an elegant state estimation method which considers the available non-linear and inequality constraint information. A truncated unscented particle filter method is proposed in this paper.This method applies the particle filtering to cope with non-linear models and non-Gaussian state distribution. Different from other particle filtering schemes, a truncated unscented Kalman filter is applied as the importance function for sampling new particles, in order to incorporate both the measurement and constraint information. Therefore, more effective particles are generated and a better state estimation result is then obtained. The advantages of the proposed truncated unscented particle filter algorithm over the state-of-the-art ones are presented by multiple Monte-Carlo simulations