Improved unscented particle filter for nonlinear bayesian estimation

The idea of particle filter is to represent probability density function (PDF) of nonlinear/non-Gaussian system by a set of random samples. One of the key issue of particle filter is the proposal distribution. In this paper, the iterated unscented Kalman filter (IUKF) is used to generate the proposal distribution for particle filter. The proposal distributions integrate the current observation, thus greatly improving the filter performance. To evaluate the efficacy of the new algorithm, we apply it in a real-world estimation problem. The simulations results are compared against those of the widely used unscented particle filter (UPF), the extended Kalman particle filter (PF-EKF) and have demonstrated superior estimating performance.