The Unscented Particle Filter 论文

摘要

In this paper we propose a novel method for nonlinear, non-Gaussian, on-line estimation. The algorithm consists of a particle filter that uses an unscented Kalman filter (UKF) to generate the importance proposal distribution. The UKF allows the particle filter to incorporate the latest observations into a prior updating routine. In addition, the UKF generates proposal distributions that match the true posterior more closely and also has the capability of generating heavier tailed distributions than the well known extended Kalman filter. As a result, the convergence results predict that the new filter should outperform standard particle filters, extended Kalman filters and unscented Kalman filters. A few experiments confirm this prediction.