Unscented Particle Filtering based on Particle Selection and Weight Optimization for Nonlinear Estimation

Particle filtering has the phenomenon of particle degradation. In order to reduce the impact of this phenomenon, this paper studies the basic unscented particle filter and resampling methods. However, the resampling techniques will bring about the particle depletion. Aiming at this problem, this paper proposes an improved unscented particle filtering algorithm to enhance the filtering estimation performance, which improves the resampling method based on particle selection and weight optimization to increase the diversity of particles. Simultaneously, the proposed algorithm is verified by simulations of the non-linear systems. The simulation results indicate that the proposed algorithm has a higher filtering accuracy than the traditional unscented particle filter.