Gaussian sum quadrature particle filtering
Liangqun Li, Zhenglong Yi, Weixin Xie · 2014
For the nonlinear and non-Gaussian filtering problem of target tracking, a novel Gaussian sum quadrature particle filter(GSQPF) based on Gauss-Hermite quadrature and Gaussian sum particle filter is proposed. In the proposed algorithm, according to the advantage of Gaussian-Hermite quadrature points in the nonlinear approximation and the diversity of quadrature points, we introduce a set of quadrature point probability densities to approximate the important density function, the filtering and prediction densities are approximated as finite Gaussian mixtures. Because of the advantage of Gaussian mixture and the particle filtering, it can effectively improve the performance. The simulations show that the presented filter can outperform both Gaussian sum particle filter(GSPF) and quadrature particle filter(QPF).