一种新的进化裂变粒子滤波算法

Novel particle filter algorithm based on evolution fission

针对经典自举粒子滤波中的重要性函数选取和重采样所导致的样本枯竭问题,提出了一种基于进化裂变的改进粒子滤波算法.该算法首先采用无迹卡尔曼滤波算法产生重要性函数,然后对重要性采样粒子进行裂变通过进化策略更新粒子集以增加粒子多样性,从而克服经典自举滤波重采样过程中的粒子退化问题.仿真实验表明,该算法能有效地提高跟踪精度,跟踪性能优于经典粒子滤波算法.

In order to overcome the choice of the importance function and the sample impoverishment after resampling,an improved particle filter algorithm based on evolution fission is presented.The algorithm uses the UKF to generate the importance function and updates the samples based on evolution fission, which could increase the diversity of the samples and overcome the degeneration of the typical particle filter. Simulation results prove that the presented algorithm has a high tracking accuracy and a good tracking performance compared to the typical particle filter.