一种新的基于交互多模型的序贯重要采样算法

通过将交互多模型（IMM）算法和粒子滤波（SIS）算法结合，提出了一种新的IMM～SIS算法。在每个模型中，都有一个标准的粒子滤波器，模型之间的交互与传统的IMM一样。由于在新的算法中，每个模型中粒子滤波都保证固定数量的粒子，因此不会出现粒子退化和贫乏现象。仿真证明了新的IMM—SIS算法在收敛速度和精度方面都要优于传统的IMM—EKF算法。

A Novel Sequential Importance Sampling Algorithm Based on Interacting Multiple Model

A novel method for multiple model particle filtering for Markovian switching systems is introduced.This new method is a combination of the interacting multiple model(IMM)filter and particle filter.A regularized particle filter is running in every mode.The mixing and interaction is similar to that in a conventional IMM filter.Furthermore,the new method keeps a fixed number of particles in each mode,and therefore it avoids the particle degeneracy and impoverishment phenomenon.Simulations show that the tracking speed and accuracy of the IMM-SIS algorithm are better than those of the IMM-EKF.