Improved unscented Kalman particle filter

Abstract

In order to improve tracking estimation accuracy of existing unscented Kalman particle filter (UPF), an improved particle filter algorithm based on iterative measurement update UKF is proposed. The algorithm uses maximum posteriori estimate of iterative unscented Kalman filter as the important density function of the particle filter and amends the state covariance using Levenberg-Marquardt method. So the observed information of particle is effectively used. This will be more consistent with the posterior probability distribution of true state. Simulation results show that estimation performance of the proposed algorithm is much better than both standard particle filter (PF) and unscented particle filter (UPF).