一种改进重采样的粒子滤波算法

Particle filter algorithm based on improved resampling

针对粒子滤波重采样过程中存在的粒子多样性丧失问题,提出一种改进重采样的粒子滤波算法。按照局部重采样算法对粒子进行分类,中等权值的粒子保持不变,大、小两种权值的粒子采用Thompson-Taylor算法进行随机线性组合产生新粒子。实验结果表明,该算法能在降低计算复杂度的同时不丧失粒子多样性,提高了滤波性能。

In order to solve the loss of particle diversity exiting in resampling process of particle filter, this paper presented a particle filter algorithm based on improved resampling. It classified the particles to different groups according to partial resam- piing. It kept the particles with medium weight values same, and combined the other two groups with high and loiw weight val- ues linearly and randomly to generate new particles using Thompson-Taylor algorithm. Experimental results show that the im- proved algorithm can reduce computational complexity and keep the diversity of particles and it also enhances the performance of filter.