The Semi-Iterative Unscented Particle Filtering

Abstract

Particle filtering algorithm has been widely used in solving nonlinear/non Gaussian filtering problems. In this paper, a novel filtering method –mixed unscented particle filtering (MUPF) for nonlinear dynamic systems is proposed. MUPF mainly includes two steps. In the first step, unscented extended Kalman filter was used as proposal distribution to generate particles; then in the second step, after getting means and variances of the proposal distribution, these particles were refined using unscented transformation. To reduce the calculating time, only part of these particles will be refined according to some special rules. This process can be regarded as mixed unscented transformation (MUT). The proposed MUPF algorithm was compared with other five filtering algorithms and the simulating results show that means and variances of MUPF are lower than other filtering algorithms. Index Terms – filtering algorithm, particle filtering, unscented Kalman filter, adaptive factor.